While the cloud is often safer than on-premises computing, it’s still vulnerable to a wide range of security threats. In 2023, 39% of businesses were reported to have experienced a breach in their cloud environment last year, up from 35% the year before.

The challenge for cloud security teams is to embrace the benefits of cloud computing while safeguarding their companies’ digital assets. 

As such, understanding and adapting to the latest cloud security trends is critical. Equally vital is the need for cloud security teams to continually uplevel individual technical skills to keep up with the latest security threats. 

Read on to learn more about the current trends shaping cloud security and explore how these trends will impact the technical skills needed to keep your organization and its data secure. 

What Is Cloud Security?

 A surprising number of organizations that use the cloud haven’t taken the necessary precautions to protect their sensitive data. While 75% of organizations report that 40% or more of their data in the cloud is sensitive, less than half of this data is encrypted. Given that the number of global cyberattacks can increase by 38% in a single year, protecting this data is vital.

Cloud security is the discipline charged with protecting the data, applications, and infrastructure hosted in these cloud environments from potential threats and vulnerabilities. Cloud security is a critical aspect of cloud computing, as organizations increasingly rely on cloud services to store and process sensitive information. The primary goal of cloud security is to ensure the confidentiality, integrity, and availability of data and resources in the cloud.

Top Cloud Security Trends

As cybersecurity threats evolve, organizations and industry professionals also need to look at security measures and adapt their skills to keep up. The best way to do so is by proactively responding to the many emerging trends taking shape across the industry.

#1. Zero-Trust Architecture

The traditional network security perimeter is becoming obsolete. In the past, organizations relied heavily on a well-defined perimeter to safeguard their digital assets. However, the rise of sophisticated cyber attacks, insider threats, and the increasing prevalence of remote work have collectively rendered the traditional perimeter defenses inadequate.

Zero trust architecture challenges the assumption that entities within the network, once verified, can inherently be trusted. Instead, it operates on the principle of “never trust, always verify.” Whether it be the threat of bad actors, or simply the existence of human error, every user, device, or application is treated as potentially untrusted. This trend requires a shift in mindset, with a focus on continuous verification of identity and strict access controls.

#2. Multi-Cloud Security

The adoption of multi-cloud environments represents a strategic response to the diverse needs and requirements of modern organizations. As businesses increasingly rely on cloud services for various aspects of their operations, the utilization of multiple cloud providers becomes a pragmatic approach. The rationale behind multi-cloud adoption is often rooted in the desire to avoid vendor lock-in, optimize costs, and capitalize on the unique strengths of different cloud providers. 

With organizations leveraging multiple cloud providers, ensuring consistent security across these environments is crucial. Professionals need expertise in managing security protocols and solutions that transcend the boundaries of individual cloud platforms.

#3. AI and Machine Learning in Security

The integration of AI and machine learning (ML) into the realm of cybersecurity marks a paradigm shift in the way organizations defend against increasingly sophisticated cyber threats. These tools empower security systems to evolve from rule-based, reactive measures to proactive, adaptive defense mechanisms. The ability of these systems to analyze vast amounts of data, recognize patterns, and discern anomalies in real time significantly enhances the detection and mitigation of cyber threats. 

In the context of cloud security, where the scale and diversity of data are intense, harnessing the power of AI and ML for threat detection and analysis becomes paramount. Managing AI-driven security solutions requires a holistic understanding of the organization’s infrastructure, data flows, and application landscape. Professionals must be adept at configuring, monitoring, and fine-tuning AI algorithms, as well as skilled in interpreting the insights generated by these models, in order to translate them into actionable intelligence for a timely and effective response.

#4. DevSecOps Integration

The integration of security into the DevOps pipeline, known as DevSecOps, is a transformative approach that places security at the core of the DevOps lifecycle. This shift represents a departure from the traditional paradigm where security was often treated as an afterthought, relegated to the final stages of the development process, which often led to vulnerabilities persisting through multiple development cycles. 

Instead, the integration of security into DevOps involves automating security processes, incorporating security testing into the continuous integration/continuous deployment (CI/CD) pipeline, and fostering a culture where security is everyone’s responsibility. This requires proficiency in tools and technologies that facilitate automated testing, vulnerability scanning, and code analysis. Additionally, professionals operating in the DevSecOps space must collaborate with development and operations teams, breaking down silos that traditionally separated these functions – ensuring that security is not just a checkbox, but a shared responsibility throughout the development lifecycle.

#5. Cloud-Native Security

The surge in popularity of cloud-native architectures signifies a transformative shift in how applications are designed, built, and deployed. They are designed to take full advantage of cloud-computing environments, thus providing security beyond traditional architectures. In this landscape, understanding the intricacies of securing cloud-native components such as microservices, containers, and serverless computing is not just a best practice; it’s a non-negotiable for organizations embracing the agility and scalability offered by cloud environments.

Cloud-native security requires a holistic understanding of the entire application landscape. Professionals must collaborate closely with development and operations teams, ensuring that security considerations are an integral part of the design and implementation. The ability to navigate the complexities of this environment and its components is not only a skill set, but a strategic advantage for organizations seeking to harness the full potential of cloud-native technologies securely. 

#6. IoT Security

The proliferation of Internet of Things (IoT) devices represents a technological revolution, connecting everyday objects to the internet and transforming them into intelligent, data-generating entities. However, this interconnected ecosystem also introduces unprecedented security challenges. As IoT devices become ubiquitous, organizations must recognize and address the new entry points for potential security breaches that arise from the sheer scale and diversity of these interconnected devices. 

Professionals in cloud security play a critical role in mitigating the risks associated with IoT deployments. Unlike traditional computing environments, IoT ecosystems encompass a vast array of devices with varying levels of computing power, communication protocols, and security postures. Cloud-security experts need to be adept at implementing robust and adaptive security measures that account for IoT devices.

#7. End-to-End Encryption

With an increasing emphasis on data privacy, the trend toward end-to-end encryption (E2EE) is picking up speed, marking a fundamental shift in how organizations safeguard their sensitive information. This encryption paradigm, where data is securely encrypted throughout its entire journey, is gaining momentum as a proactive measure to counteract the ever-present threats of unauthorized access and data breaches.

End-to-end encryption extends beyond the traditional focus on securing data in transit. While protecting information as it moves between devices or across networks remains crucial, the trend recognizes the need for a more comprehensive approach. Cloud-security professionals are now tasked with implementing encryption measures that span the entire data lifecycle – encompassing data at rest, in transit, and within applications and databases.

#8. Evolution of Identity and Access Management

As the traditional network perimeter becomes porous and digital ecosystems grow in complexity, identity and access management (IAM) emerges as a linchpin in safeguarding sensitive data, applications, and resources from potential threats. IAM’s evolution is driven by the critical need to go beyond conventional username-password authentication methods. Instead it serves as a strategic response to the sophisticated tactics employed by bad actors, recognizing that static credentials alone are often insufficient to protect against increasingly sophisticated attacks. 

Cloud-security professionals are witnessing a shift towards more advanced IAM solutions that incorporate cutting edge technologies – think biometrics, adaptive authentication, and continuous monitoring – to enhance the granularity and resilience of access controls. To stay ahead of these IAM advancements, it’s critical to remain proactive and stay well-informed about emerging technologies, industry best practices, and evolving threats. 

#9. Serverless Security Challenges

Serverless computing is gaining popularity. While lauded for its scalability, cost-effectiveness, and streamlined development, it also introduces a distinctive set of emerging security challenges that demand the attention of cloud-security professionals.

Unlike traditional monolithic applications, serverless functions operate independently and are often executed in ephemeral containers. This requires cloud-security experts to focus on implementing robust authentication and authorization mechanisms, ensuring only authorized entities can invoke and interact with these functions. 

Monitoring for potential vulnerabilities in a serverless environment presents a unique challenge. Traditional security tools may not seamlessly integrate with the event-driven nature of serverless architectures. Cloud-security experts need to deploy specialized monitoring solutions capable of providing real-time insights into the execution and performance of serverless functions. By leveraging these serverless-specific security tools, professionals can detect anomalies, unauthorized access attempts, and potential security breaches, allowing for swift responses to emerging threats.

#10. Regulatory Compliance in the Cloud

Regulations for data protection have had a centralizing impact on cloud security. Organizations now have to navigate a complex web of global regulations to ensure the secure and compliant handling of sensitive information. As data breaches and privacy concerns escalate, compliance with regulations like GDPR, HIPAA, and others has become paramount, turning regulatory adherence into a critical facet of cloud-security strategy.

The migration of data and applications to the cloud introduces complexities in ensuring compliance with these regulations. Failure to do so can result in legal, financial, and reputational repercussions. Cloud service providers play a role in managing the security of the underlying infrastructure, but organizations bear the responsibility for securing their applications and data within the cloud environment. Cloud-security professionals are at the forefront of addressing this challenge, wielding technical skills to implement and maintain robust compliance measures tailored to the specific requirements of each regulation.

The post Top 10 Cloud Security Trends and How They’ll Impact Technical Skills appeared first on HackerRank Blog.

Google Cloud Platform, or GCP. While it may have been a latecomer to the cloud party compared to AWS and Azure, don’t let that fool you. GCP has managed to carve a unique identity, packed with a wide range of features and an open-source spirit that aligns closely with modern development cultures. More than just another cloud provider, it’s a comprehensive suite of solutions that cater to varied business needs, from startups grappling with scale to Fortune 500 companies managing vast, complex architectures.

In this post, we’ll unpack what GCP is, delve into its key features, discuss the skill set needed to master it, and shed some light on the hiring outlook for those armed with GCP expertise. 

What is Google Cloud Platform?

When most people hear “Google,” they think of the search engine that’s virtually synonymous with the internet itself, or perhaps Android, the popular mobile operating system. But Google’s reach goes far beyond that, extending into the realm of cloud computing with Google Cloud Platform, or GCP for short. Launched in 2011, GCP might have been late to the cloud party compared to Amazon’s AWS (which emerged in 2006), but it came in strong, leveraging years of experience running high-traffic, high-availability services like Google Search, YouTube, and Gmail.

So what exactly is GCP? At its most basic, Google Cloud Platform is a collection of cloud computing services. It allows users to do everything from hosting websites and applications to crunching big data to building and implementing machine learning models. But saying that GCP is a collection of cloud services is a bit like saying a Swiss army knife is a cutting tool — it’s accurate but fails to capture the versatility and depth of what’s on offer.

GCP provides a range of services under different computing models — Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and even serverless computing. It offers these services on a global network — the very same network that handles the billions of Google search queries and YouTube videos that people consume every day. This ensures both rapid and reliable service execution and delivery.

But what really sets GCP apart is its core features, which boil down to three main points:

Open Cloud: GCP is deeply committed to open-source technologies. This is evident in its robust support for Kubernetes, the open-source container orchestration platform that Google originally designed. This focus on openness allows for smoother transitions and interoperability between different cloud providers and on-premises solutions.

Data at the Core: Google’s heritage is all about handling data, whether it’s sorting it, analyzing it, or making it useful. This data-centric ethos is woven into every fiber of GCP, especially in its array of database solutions, data analytics tools, and machine learning services.

Security First: Given its history of managing massive amounts of sensitive consumer data, Google naturally places a high premium on security. This is manifested in GCP’s stringent identity management protocols and extensive network security features.

By now, you should have a sense that GCP isn’t merely playing catch-up; it’s a serious contender with its own unique strengths and offerings. As it continues to grow in market share, so does the range of career opportunities for professionals skilled in navigating this expansive platform.

Key GCP Offerings

Google Cloud Platform is more than just a sum of its parts — it’s a cohesive toolkit designed to solve complex problems in modern computing. Here’s a rundown of some of its standout service offerings.

Compute Engine: Virtual Machines

Starting with the basics, Compute Engine allows users to deploy virtual machines (VMs) that are tailored to their needs. Need a Linux-based machine with specific CPU and memory requirements? No problem. Compute Engine gives users that flexibility while providing the benefits of Google’s infrastructure, like faster disk speeds and global reach.

App Engine: Platform as a Service

For those who aren’t keen on managing their own servers and just want to focus on their code, App Engine is the answer. It’s a fully managed platform that takes care of all the underlying infrastructure, so users can deploy web apps and APIs with ease. And it scales automatically, meaning if an app suddenly goes viral, its developers won’t be up all night figuring out how to handle the traffic.

Kubernetes Engine: Managed Kubernetes

Born from Google’s experience with containers, Kubernetes Engine is a managed Kubernetes service that enables users to deploy, manage, and scale containerized applications. For businesses that have a microservices architecture — or are moving in that direction — Kubernetes Engine simplifies their workflow dramatically.

Cloud Functions: Serverless Architecture

For those times when a user needs to run a function in response to an event — like processing an image upload or handling an API request — Cloud Functions comes into play. It’s a serverless platform that automatically scales the compute resources, so users only pay for the compute time they actually use.

Cloud Storage and Databases

GCP offers a broad range of storage options to suit different needs. Cloud Storage for object storage, Cloud SQL for relational databases, and Firestore for NoSQL needs are just a few examples. This flexibility makes it easier for users to design an architecture that fits the way their application works.

BigQuery: Data Analytics

BigQuery takes data analytics to the next level. It allows users to run super-fast queries on massive datasets, all without having to manage any infrastructure. It’s like having a supercomputer at your fingertips, only better because it’s in the cloud.

Machine Learning and AI Services

GCP’s machine learning and AI capabilities are among its standout features. Whether you’re a seasoned data scientist or a developer wanting to integrate machine learning into your app, services like AutoML and TensorFlow make it possible.

Networking Features

Google’s robust global network is one of GCP’s unsung heroes. Load balancing, CDN capabilities, and VPC (virtual private cloud) are all part of the package, ensuring that services are fast, secure, and scalable.

Security and Identity Features

We touched on this briefly before, but it’s worth reiterating. GCP has robust security protocols, with end-to-end encryption, identity and access management, and numerous compliance certifications to protect sensitive data.

Open-Source Integrations

The affinity for open-source solutions isn’t just a philosophy; it’s a feature. GCP offers various integrations with open-source platforms, making it easier to bring your existing tools into the cloud environment.

Must-Have Skills for GCP

Understanding Google Cloud Platform isn’t just about knowing what each service does; it’s about knowing how to integrate these services to build comprehensive solutions. These skills are invaluable for anyone looking to master GCP.

Cloud Fundamentals

Before you dive into GCP-specific services, a solid understanding of cloud computing basics is essential. Concepts like virtualization, containerization, and distributed computing will give you a sturdy foundation to build upon.

Infrastructure and Deployment

Knowing how to set up and manage a virtual machine on Compute Engine or how to deploy a web application on App Engine can be critical. You should be comfortable with command-line tools as well as GCP’s console.

DevOps and Automation

The cloud is most effective when you can automate repetitive tasks. Skills in continuous integration and continuous deployment (CI/CD) are valuable. Familiarity with tools like Jenkins, GitLab, or GCP’s own Cloud Build can go a long way.

Containerization

Given GCP’s strong support for Kubernetes, understanding containerization technologies like Docker is a big plus. This is particularly important if you’re dealing with microservices architectures or want to ensure application portability.

Data Management

Whether it’s storing data in Cloud SQL, a relational database service, or dealing with NoSQL databases like Firestore, understanding data storage, retrieval, and manipulation is key. Also, skills in data analytics can be a huge asset, especially with tools like BigQuery.

Programming Languages

GCP supports a variety of programming languages like Python, Java, Go, and Node.js. The more languages you or your developers are comfortable with, the more versatile your solutions can be.

Networking

A grasp of networking basics like HTTP/HTTPS, VPNs, and VPCs can be beneficial. Google Cloud Platform offers advanced networking features, and knowing how to implement these can make your applications more secure and efficient.

Security Protocols

Security should be a priority, not an afterthought. Understanding identity and access management, encryption protocols, and general cybersecurity best practices can protect your resources and data.

Machine Learning and AI

If you’re looking to implement machine learning models, a basic understanding of machine learning algorithms and experience with tools like TensorFlow will be invaluable. GCP’s machine learning services are user-friendly but can be powerful in the hands of those who know what they’re doing.

Soft Skills

Last but not least, effective communication, problem-solving abilities, and a knack for innovation can make your technical skills even more impactful. After all, technology is as much about people as it is about computers.

Developing proficiency in these areas can significantly up your GCP game, whether you’re a developer, a cloud architect, or a DevOps engineer. And for hiring managers, this list can serve as a useful guide for what to look for when bringing new talent on board.

The Hiring Outlook for GCP Skills

The reverberations of Google Cloud Platform’s growth are unmistakable in the job market. As the cloud becomes an integral part of business operations across sectors, the hunger for GCP expertise is intensifying. 

According to the 2022 Global Knowledge IT Skills and Salary Report, Google Cloud certifications such as Professional Cloud Architect and Professional Data Engineer are some of the highest-paying certifications in North America, garnering average annual salaries of $154,234 and $148,682 respectively, and reflecting the high demand for GPC skills. It’s not just about specialized roles either; the demand for GCP know-how spans multiple job titles, from DevOps engineers responsible for automation and deployments to SysOps administrators who ensure the smooth running of cloud services on GCP.

The significance of cloud computing, and GCP skills in particular, can’t be understated. For tech mavens, proficiency in GCP offers a gateway to a rewarding career, flush with opportunities for innovation and growth. For those in hiring roles, pinpointing and securing GCP talent is less a luxury and more a critical ingredient for staying competitive.

This article was written with the help of AI. Can you tell which parts?

The post What Is GCP? A Guide to Google’s Cloud Universe appeared first on HackerRank Blog.

Cloud technology is far more than just an industry buzzword these days; it’s the backbone of modern IT infrastructures. And among the crowded field of cloud service providers, a handful of tech companies have emerged as key players. Microsoft’s Azure, with its enormous range of services and capabilities, has solidified its position in this global market, rivaling giants like AWS and Google Cloud and quickly becoming a favorite among both businesses and developers at the forefront of cloud-based innovation. 

As Azure continues to expand its footprint across industries, the demand for professionals proficient in its ecosystem is growing too. As a result, interviews that dive deep into Azure skills are becoming more common — and for a good reason. These interviews don’t just test a candidate’s knowledge; they probe for hands-on experience and the ability to leverage Azure’s powerful features in real-world scenarios.

Whether you’re a developer eyeing a role in this domain or a recruiter seeking to better understand the technical nuances of Azure, it can be helpful to delve into questions that capture the essence of Azure’s capabilities and potential challenges. In this guide, we unravel what Azure really is, the foundations of an Azure interview, and of course, a curated set of coding questions that every Azure aficionado should be prepared to tackle.

What is Azure?

Azure is Microsoft’s answer to cloud computing — but it’s also much more than that. It’s a vast universe of interconnected services and tools designed to meet a myriad of IT needs, from the basic to the complex.

More than just a platform, Azure offers Infrastructure-as-a-Service (IaaS), providing essential resources like virtual machines and networking. It delves into Platform-as-a-Service (PaaS), where services such as Azure App Service or Azure Functions let you deploy applications without getting bogged down by infrastructure concerns. And it has software-as-a-Service (SaaS) offerings like Office 365 and Dynamics 365.

Yet, Azure’s capabilities don’t end with these three service models. It boasts specialized services for cutting-edge technologies like IoT, AI, and machine learning. From building an intelligent bot to managing a fleet of IoT devices, Azure has tools and services tailor-made for these ventures.

What an Azure Interview Looks Like

An interview focused on Azure isn’t just a test of your cloud knowledge; it’s an exploration of your expertise in harnessing the myriad services and tools that Azure offers. Given the platform’s vast expanse, the interview could span a range of topics. It could probe your understanding of deploying and configuring resources using the Azure CLI or ARM templates. Or it might assess your familiarity with storage solutions like Blob, Table, Queue, and the more recent Cosmos DB. Networking in Azure, with its virtual networks, VPNs, and Traffic Manager, is another crucial area that interviewers often touch upon. And with the increasing emphasis on real-time data and AI, expect a deep dive into Azure’s data and AI services, like machine learning or Stream Analytics.

While the nature of questions can vary widely based on the specific role, there are some common threads. Interviewers often look for hands-on experience, problem-solving ability, and a sound understanding of best practices and architectural designs within the Azure ecosystem. For instance, if you’re aiming for a role like an Azure solutions architect, expect scenarios that challenge your skills in designing scalable, resilient, and secure solutions on Azure. On the other hand, Azure DevOps engineers might find themselves solving automation puzzles, ensuring smooth CI/CD pipelines, or optimizing infrastructure as code.

But it’s not all technical! Given that Azure is often pivotal in business solutions, you might also be tested on your ability to align Azure’s capabilities with business goals, cost management, or even disaster recovery strategies.

1. Deploy a Web App Using Azure CLI

The Azure command-line interface (CLI) is an essential tool for developers and administrators to manage Azure resources. This question tests a candidate’s proficiency with Azure CLI commands, specifically focusing on deploying web applications to Azure.

Task: Write an Azure CLI script to deploy a simple web app using Azure App Service. The script should create the necessary resources, deploy a sample HTML file, and return the public URL of the web app.

Input Format: The script should accept the following parameters:

• Resource group name
• Location (e.g., “East U.S.”)
• App service plan name
• Web app name

Constraints:

• The web app should be hosted on a free tier App Service plan.
• The HTML file to be deployed should simply display “Hello Azure!”

Output Format: The script should print the public URL of the deployed web app.

Sample Code:

#!/bin/bash

# Parameters

resourceGroupName=$1

location=$2

appServicePlanName=$3

webAppName=$4

# Create a resource group

az group create --name $resourceGroupName --location $location

# Create an App Service plan on Free tier

az appservice plan create --name $appServicePlanName --resource-group $resourceGroupName --sku F1 --is-linux

# Create a web app

az webapp create --name $webAppName --resource-group $resourceGroupName --plan $appServicePlanName --runtime "NODE|14-lts"

# Deploy sample HTML file

echo "<html><body><h1>Hello Azure!</h1></body></html>" > index.html

az webapp up --resource-group $resourceGroupName --name $webAppName --html

# Print the public URL

echo "Web app deployed at: https://$webAppName.azurewebsites.net"

Explanation:

The script begins by creating a resource group using the provided name and location. It then creates an App Service plan on the free tier. Subsequently, a web app is created using Node.js as its runtime (although we’re deploying an HTML file, the runtime is still needed). A sample HTML file is then generated on the fly with the content “Hello Azure!” and deployed to the web app using `az webapp up`. Finally, the public URL of the deployed app is printed.

2. Configure Azure Blob Storage and Upload a File

Azure Blob Storage is a vital service in the Azure ecosystem, allowing users to store vast amounts of unstructured data. This question examines a developer’s understanding of Blob Storage and their proficiency in interacting with it programmatically.

Task: Write a Python script using Azure SDK to create a container in Azure Blob Storage, and then upload a file to this container.

Input Format: The script should accept the following parameters:

• Connection string
• Container name
• File path (of the file to be uploaded)

Constraints:

• Ensure the container’s access level is set to “Blob” (meaning the blobs/files can be accessed, but not the container’s metadata or file listing).
• Handle potential exceptions gracefully, like invalid connection strings or file paths.

Output Format: The script should print the URL of the uploaded blob.

Sample Code:

from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient

def upload_to_blob(connection_string, container_name, file_path):

    try:
        # Create the BlobServiceClient

        blob_service_client = BlobServiceClient.from_connection_string(connection_string)

        # Create or get container

        container_client = blob_service_client.get_container_client(container_name)

        if not container_client.exists():

            blob_service_client.create_container(container_name, public_access='blob')

        # Upload file to blob

        blob_client = blob_service_client.get_blob_client(container=container_name, blob=file_path.split('/')[-1])

        with open(file_path, "rb") as data:

            blob_client.upload_blob(data)

        print(f"File uploaded to: {blob_client.url}")     

    except Exception as e:

        print(f"An error occurred: {e}")
# Sample Usage

# upload_to_blob('<Your Connection String>', 'sample-container', 'path/to/file.txt')

Explanation:

The script uses the Azure SDK for Python. After establishing a connection with the Blob service using the provided connection string, it checks if the specified container exists. If not, it creates one with the access level set to “Blob.” The file specified in the `file_path` is then read as binary data and uploaded to the blob storage. Once the upload is successful, the URL of the blob is printed. Any exceptions encountered during these operations are caught and printed to inform the user of potential issues.

3. Azure Functions: HTTP Trigger with Cosmos DB Integration

Azure Functions, known for its serverless compute capabilities, allows developers to run code in response to specific events. Cosmos DB, on the other hand, is a multi-model database service for large-scale applications. This question assesses a developer’s ability to create an Azure Function triggered by an HTTP request and integrate it with Cosmos DB.

Task: Write an Azure Function that’s triggered by an HTTP GET request. The function should retrieve a document from an Azure Cosmos DB based on a provided ID and return the document as a JSON response.

Input Format: The function should accept an HTTP GET request with a query parameter named `docId`, representing the ID of the desired document.

Output Format: The function should return the requested document in JSON format or an error message if the document isn’t found.

Constraints:

• Use the Azure Functions 3.x runtime.
• The Cosmos DB has a database named `MyDatabase` and a container named `MyContainer`.
• Handle exceptions gracefully, ensuring proper HTTP response codes and messages.

Sample Code:

using System.IO;

using Microsoft.AspNetCore.Mvc;

using Microsoft.Azure.WebJobs;

using Microsoft.Azure.WebJobs.Extensions.Http;

using Microsoft.AspNetCore.Http;

using Microsoft.Extensions.Logging;

using Newtonsoft.Json;

using Microsoft.Azure.Documents.Client;

using Microsoft.Azure.Documents.Linq;

using System.Linq;

public static class GetDocumentFunction

{

    [FunctionName("RetrieveDocument")]

    public static IActionResult Run(

        [HttpTrigger(AuthorizationLevel.Function, "get", Route = null)] HttpRequest req,

        [CosmosDB(

            databaseName: "MyDatabase",

            collectionName: "MyContainer",

            ConnectionStringSetting = "AzureWebJobsCosmosDBConnectionString",

            Id = "{Query.docId}")] dynamic document,

        ILogger log)

    {

        log.LogInformation("C# HTTP trigger function processed a request.");

        if (document == null)

        {

            return new NotFoundObjectResult("Document not found.");

        }

        return new OkObjectResult(document);
    }
}

Explanation:

This Azure Function uses the Azure Functions 3.x runtime and is written in C#. It’s triggered by an HTTP GET request. The function leverages the CosmosDB binding to fetch a document from Cosmos DB using the provided `docId` query parameter. If the document exists, it’s returned as a JSON response. Otherwise, a 404 Not Found response is returned with an appropriate error message.

Note: This code assumes the Cosmos DB connection string is stored in an application setting named “AzureWebJobsCosmosDBConnectionString.”

4. Azure Virtual Machine: Automate VM Setup with Azure SDK for Python**

Azure Virtual Machines (VMs) are a fundamental building block in the Azure ecosystem. It’s crucial for developers to know how to automate VM creation and setup to streamline operations and ensure standardized configurations. This question assesses a developer’s understanding of the Azure SDK for Python and their ability to automate VM provisioning.

Task: Write a Python script using the Azure SDK to create a new virtual machine. The VM should run Ubuntu Server 18.04 LTS, and once set up, it should automatically install Docker.

Input Format: The script should accept the following parameters:

• Resource group name
• VM name
• Location (e.g., “East U.S.”)
• Azure subscription ID
• Client ID (for Azure service principal)
• Client secret (for Azure service principal)
• Tenant ID (for Azure service principal)

Constraints:

• Ensure the VM is of size `Standard_DS1_v2`.
• Set up the VM to use SSH key authentication.
• Assume the SSH public key is located at `~/.ssh/id_rsa.pub`.
• Handle exceptions gracefully.

Output Format: The script should print the public IP address of the created VM.

Sample Code:

from azure.identity import ClientSecretCredential

from azure.mgmt.compute import ComputeManagementClient

from azure.mgmt.network import NetworkManagementClient

from azure.mgmt.resource import ResourceManagementClient




def create_vm_with_docker(resource_group, vm_name, location, subscription_id, client_id, client_secret, tenant_id):

    # Authenticate using service principal

    credential = ClientSecretCredential(client_id=client_id, client_secret=client_secret, tenant_id=tenant_id)

    # Initialize management clients

    resource_client = ResourceManagementClient(credential, subscription_id)

    compute_client = ComputeManagementClient(credential, subscription_id)

    network_client = NetworkManagementClient(credential, subscription_id)

    # Assuming network setup, storage, etc. are in place

    # Fetch SSH public key

    with open("~/.ssh/id_rsa.pub", "r") as f:

        ssh_key = f.read().strip()

    # Define the VM parameters, including post-deployment script to install Docker

    vm_parameters = {

        #... (various VM parameters like size, OS type, etc.)

        'osProfile': {

            'computerName': vm_name,

            'adminUsername': 'azureuser',

            'linuxConfiguration': {

                'disablePasswordAuthentication': True,

                'ssh': {

                    'publicKeys': [{

                        'path': '/home/azureuser/.ssh/authorized_keys',

                        'keyData': ssh_key

                    }]

                }

            },

            'customData': "IyEvYmluL2Jhc2gKc3VkbyBhcHQtZ2V0IHVwZGF0ZSAmJiBzdWRvIGFwdC1nZXQgaW5zdGFsbCAt

            eSBkb2NrZXIuY2U="  # This is base64 encoded script for "sudo apt-get update && sudo apt-get install -y docker.ce"

        }

    }

    # Create VM

    creation_poller = compute_client.virtual_machines.create_or_update(resource_group, vm_name, vm_parameters)

    creation_poller.result()

    # Print the public IP address (assuming IP is already allocated)

    public_ip = network_client.public_ip_addresses.get(resource_group, f"{vm_name}-ip")

    print(f"Virtual Machine available at: {public_ip.ip_address}")

# Sample Usage (with parameters replaced appropriately)

# create_vm_with_docker(...)

Explanation:

The script begins by establishing authentication using the provided service principal credentials. It initializes management clients for resource, compute, and networking operations. After setting up networking and storage (which are assumed to be in place for brevity), the VM is defined with the necessary parameters. The post-deployment script installs Docker on the VM upon its first boot. Once the VM is created, its public IP address is printed.

Note: The Docker installation script is base64 encoded for brevity. In real use cases, you might use cloud-init or other provisioning tools for more complex setups.

5. Azure SQL Database: Data Migration and Querying

Azure SQL Database is a fully managed relational cloud database service for developers. The integration between applications and data becomes crucial, especially when migrating data or optimizing application performance through SQL queries.

Task: Write a Python script that does the following:

1. Connects to an Azure SQL Database using provided connection details
2. Migrates data from a CSV file into a table in the Azure SQL Database
3. Runs a query on the table to fetch data based on specific criteria

Input Format: The script should accept command line arguments in the following order:

• Connection string for the Azure SQL Database
• Path to the CSV file
• The query to run on the table

Constraints:

• The CSV file will have headers that match the column names of the target table.
• Handle exceptions gracefully, such as failed database connections, invalid SQL statements, or CSV parsing errors.

Output Format: The script should print:

• A success message after data has been migrated
• The results of the SQL query in a readable format

Sample Code:

import pyodbc

import csv

import sys

def migrate_and_query_data(conn_string, csv_path, sql_query):

    try:

        # Connect to Azure SQL Database

        conn = pyodbc.connect(conn_string)

        cursor = conn.cursor()

        # Migrate CSV data

        with open(csv_path, 'r') as file:

            reader = csv.DictReader(file)

            for row in reader:

                columns = ', '.join(row.keys())

                placeholders = ', '.join('?' for _ in row)

                query = f"INSERT INTO target_table ({columns}) VALUES ({placeholders})"

                cursor.execute(query, list(row.values()))

        print("Data migration successful!")

        # Execute SQL query and display results

        cursor.execute(sql_query)

        for row in cursor.fetchall():

            print(row)

        conn.close()

    except pyodbc.Error as e:

        print(f"Database error: {e}")

    except Exception as e:

        print(f"An error occurred: {e}")

# Sample usage (with parameters replaced appropriately)

# migrate_and_query_data(sys.argv[1], sys.argv[2], sys.argv[3])

Explanation: 

This script utilizes the `pyodbc` library to interact with Azure SQL Database. The script starts by establishing a connection to the database and then iterates through the CSV rows to insert them into the target table. After the data migration, it runs the provided SQL query and displays the results. The script ensures that database-related errors, as well as other exceptions, are captured and presented to the user.

Note: Before running this, you’d need to install the necessary Python packages, such as `pyodbc` and ensure the right drivers for Azure SQL Database are in place.

6. Azure Logic Apps with ARM Templates: Automated Data Sync

Azure Logic Apps provide a powerful serverless framework to integrate services and automate workflows. While the Azure Portal offers a user-friendly visual designer, in professional settings, especially with DevOps and CI/CD pipelines, there’s often a need to define these workflows in a more programmatic way. Enter ARM (Azure Resource Manager) templates: a declarative syntax to describe resources and configurations, ensuring idempotent deployments across environments.

Task: Taking it up a notch from the visual designer, your challenge is to implement an Azure Logic App that automates the process of syncing data between two Azure Table Storage accounts using an ARM template. This will test both your familiarity with the Logic Apps service and your ability to translate a workflow into an ARM template.

Inputs:

• Source Azure Table Storage connection details
• Destination Azure Table Storage connection details

Constraints:

• Your ARM template should define the Logic App, its trigger, actions, and any associated resources like connectors.
• The Logic App should be triggered whenever a new row is added to the source Azure Table Storage.
• Newly added rows should be replicated to the destination Azure Table Storage without any data loss or duplication.
• Any failures in data transfer should be logged appropriately.

Sample ARM Template (simplified for brevity):

{

    "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",

    "contentVersion": "1.0.0.0",

    "resources": [

        {

            "type": "Microsoft.Logic/workflows",

            "apiVersion": "2017-07-01",

            "name": "SyncAzureTablesLogicApp",

            "location": "[resourceGroup().location]",

            "properties": {

                "definition": {

                    "$schema": "...",

                    "contentVersion": "...",

                    "triggers": {

                        "When_item_is_added": {

                            "type": "ApiConnection",

                            ...

                        }

                    },

                    "actions": {

                        "Add_item_to_destination": {

                            "type": "ApiConnection",

                            ...

                        }

                    }

                },

                "parameters": { ... }

            }

        }

    ],

    "outputs": { ... }

}

Explanation:

Using ARM templates to define Azure Logic Apps provides a programmatic and version-controllable approach to designing cloud workflows. The provided ARM template is a basic structure, defining a Logic App resource and its corresponding trigger and action for syncing data between two Azure Table Storage accounts. While the ARM template in this question is simplified, a proficient Azure developer should be able to flesh out the necessary details.

To implement the full solution, candidates would need to detail the trigger for detecting new rows in the source table, the action for adding rows to the destination table, and the error-handling logic.

Resources to Improve Azure Knowledge

• HackerRank Interview
• Tech Interview Prep: How To Ace Your Interview
• How to Prepare for a Microsoft Azure Certification Exam

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Cloud computing technology has firmly enveloped the world of tech, with Amazon Web Services (AWS) being one of the fundamental layers. Launched in 2006, AWS has evolved into a comprehensive suite of on-demand cloud computing platforms, tools, and services, powering millions of businesses globally.

The ubiquity of AWS is undeniable. As of Q1 2023, AWS commands 32% of the cloud market, underlining its pervasive influence. This widespread reliance on AWS reflects a continued demand for professionals adept in AWS services who can leverage its vast potential to architect scalable, resilient, and cost-efficient application infrastructures.

Companies are actively on the hunt for engineers, system architects, and DevOps engineers who can design, build, and manage AWS-based infrastructure, solve complex technical challenges, and take advantage of cutting-edge AWS technologies. Proficiency in AWS has become a highly desirable skill, vital for tech professionals looking to assert their cloud computing capabilities, and a critical criterion for recruiters looking to acquire top-tier talent.

In this article, we explore what an AWS interview typically looks like and introduce crucial AWS interview questions that every developer should be prepared to tackle. These questions are designed not only to test developers’ practical AWS skills but also to demonstrate their understanding of how AWS services interconnect to build scalable, reliable, and secure applications. Whether you’re a seasoned developer looking to assess and polish your AWS skills or a hiring manager seeking effective ways to evaluate candidates, this guide will prepare you to navigate AWS interviews with ease.

What is AWS?

Amazon Web Services, popularly known as AWS, is the reigning champ of cloud computing platforms. It’s an ever-growing collection of over 200 cloud services that include computing power, storage options, networking, and databases, to name a few. These services are sold on demand and customers pay for what they use, providing a cost-effective way to scale and grow.

AWS revolutionizes the way businesses develop and deploy applications by offering a scalable and durable platform that businesses of all sizes can leverage. Be it a promising startup or a Fortune 500 giant, many rely on AWS for a wide variety of workloads, including web and mobile applications, game development, data processing and warehousing, storage, archive, and many more.

What an AWS Interview Looks Like

Cracking an AWS interview involves more than just knowing the ins and outs of S3 buckets or EC2 instances. While a deep understanding of these services is vital, you also need to demonstrate how to use AWS resources effectively and efficiently in real-world scenarios.

An AWS interview typically tests your understanding of core AWS services, architectural best practices, security, and cost management. You could be quizzed on anything from designing scalable applications to deploying secure and robust environments on AWS. The level of complexity and depth of these questions will depend largely on the role and seniority level you are interviewing for.

AWS skills are not restricted to roles like cloud engineers or AWS solutions architects. Today, full-stack developers, DevOps engineers, data scientists, machine learning engineers, and even roles in management and sales are expected to have a certain level of familiarity with AWS. For instance, a full-stack developer might be expected to know how to deploy applications on EC2 instances or use Lambda for serverless computing, while a data scientist might need to understand how to leverage AWS’s vast suite of analytics tools.

That being said, irrespective of the role, some common themes generally crop up in an AWS interview. These include AWS’s core services like EC2, S3, VPC, Route 53, CloudFront, IAM, RDS, and DynamoDB; the ability to choose the right AWS services based on requirements; designing and deploying scalable, highly available, and fault-tolerant systems on AWS; data security and compliance; cost optimization strategies; and understanding of disaster recovery techniques.

1. Upload a File to S3

Amazon S3 (Simple Storage Service) is one of the most widely used services in AWS. It provides object storage through a web service interface and is used for backup and restore, data archiving, websites, applications, and many other tasks. In a work environment, a developer may need to upload files to S3 for storage or for further processing. Writing a script to automate this process can save a significant amount of time and effort, especially when dealing with large numbers of files. 

Task: Write a Python function that uploads a file to a specified S3 bucket.

Input Format: The input will be two strings: the first is the file path on the local machine, and the second is the S3 bucket name.

Output Format: The output will be a string representing the URL of the uploaded file in the S3 bucket.

Sample Code:

import boto3

def upload_file_to_s3(file_path, bucket_name):

    s3 = boto3.client('s3')

    file_name = file_path.split('/')[-1]

    s3.upload_file(file_path, bucket_name, file_name)

    file_url = f"https://{bucket_name}.s3.amazonaws.com/{file_name}"

    return file_url

Explanation:

This question tests a candidate’s ability to interact with AWS S3 using Boto3, the AWS SDK for Python. The function uses Boto3 to upload the file to the specified S3 bucket and then constructs and returns the file URL.

2. Launch an EC2 Instance

Amazon EC2 (Elastic Compute Cloud) is a fundamental part of many AWS applications. It provides resizable compute capacity in the cloud and can be used to launch as many or as few virtual servers as needed. Understanding how to programmatically launch and manage EC2 instances is a valuable skill for developers working on AWS, as it allows for more flexible and responsive resource allocation compared to manual management. 

Task: Write a Python function using Boto3 to launch a new EC2 instance.

Input Format: The input will be two strings: the first is the instance type, and the second is the Amazon Machine Image (AMI) ID.

Output Format: The output will be a string representing the ID of the launched EC2 instance.

Sample Code:

import boto3

def launch_ec2_instance(instance_type, image_id):

    ec2 = boto3.resource('ec2')

    instances = ec2.create_instances(

        ImageId=image_id,

        InstanceType=instance_type,

        MinCount=1,

        MaxCount=1

    )

    return instances[0].id

Explanation:

The function uses Boto3 to launch an EC2 instance with the specified instance type and AMI ID, and then returns the instance ID. This intermediate-level question tests a candidate’s knowledge of AWS EC2 operations. 

3. Read a File from S3 with Node.js

Reading data from an S3 bucket is a common operation when working with AWS. This operation is particularly important in applications involving data processing or analytics, where data stored in S3 needs to be loaded and processed by compute resources. In this context, AWS Lambda is often used for running code in response to triggers such as changes in data within an S3 bucket. Therefore, a developer should be able to read and process data stored in S3. 

Task: Write a Node.js AWS Lambda function that reads an object from an S3 bucket and logs its content.

Input Format: The input will be an event object with details of the S3 bucket and the object key.

Output Format: The output will be the content of the file, logged to the console.

Sample Code:

const AWS = require('aws-sdk');

const s3 = new AWS.S3();

exports.handler = async (event) => {

    const params = {

        Bucket: event.Records[0].s3.bucket.name,

        Key: event.Records[0].s3.object.key

    };

    const data = await s3.getObject(params).promise();

    console.log(data.Body.toString());

};

Explanation:

This advanced-level question requires knowledge of AWS SDK for JavaScript (in Node.js) and Lambda. The above AWS Lambda function is triggered by an event from S3. The function then reads the content of the S3 object and logs it. 

4. Write to a DynamoDB Table

Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It’s commonly used to support web, mobile, gaming, ad tech, IoT, and many other applications that need low-latency data access. Being able to interact with DynamoDB programmatically allows developers to build more complex, responsive applications and handle data in a more flexible way.

Task: Write a Python function using Boto3 to add a new item to a DynamoDB table.

Input Format: The input will be two strings: the first is the table name, and the second is a JSON string representing the item to be added.

Output Format: The output will be the response from the DynamoDB put operation.

Sample Code:

import boto3

import json

def add_item_to_dynamodb(table_name, item_json):

    dynamodb = boto3.resource('dynamodb')

    table = dynamodb.Table(table_name)

    item = json.loads(item_json)

    response = table.put_item(Item=item)

    return response

Explanation:

This function uses Boto3 to add a new item to a DynamoDB table. The function first loads the item JSON string into a Python dictionary, then adds it to the DynamoDB table. This question tests a candidate’s knowledge of how to interact with a DynamoDB database using Boto3.

5. Delete an S3 Object

Being able to delete an object from an S3 bucket programmatically is important for maintaining data hygiene and managing storage costs. For instance, you may need to delete objects that are no longer needed to free up space and reduce storage costs, or you might need to remove data for compliance reasons. Understanding how to perform this operation through code rather than manually can save a lot of time when managing large amounts of data.

Task: Write a Node.js function to delete an object from an S3 bucket.

Input Format: The input will be two strings: the first is the bucket name, and the second is the key of the object to be deleted.

Output Format: The output will be the response from the S3 delete operation.

Sample Code:

const AWS = require('aws-sdk');

const s3 = new AWS.S3();

async function delete_s3_object(bucket, key) {

    const params = {

        Bucket: bucket,

        Key: key

    };
    const response = await s3.deleteObject(params).promise();

    return response;
}

Explanation:

The function uses the AWS SDK for JavaScript (in Node.js) to delete an object from an S3 bucket and then returns the response. This expert-level question tests the candidate’s ability to perform S3 operations using the AWS SDK.

Resources to Improve AWS Knowledge

• HackerRank Interview
• Tech Interview Prep: How To Ace Your Interview
• AWS Certification Exam Preparation

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Ever marveled at how Netflix delivers your favorite shows flawlessly? Or, perhaps you’ve booked an Airbnb and wondered how they manage their vast inventory so efficiently? The credit, in large part, goes to a behind-the-scenes hero: Amazon Web Services (AWS). 

As cloud adoption has soared in recent years, AWS has become a cornerstone of many businesses, from fledgling startups to Fortune 500 giants. Its rise has been meteoric and its impact profound. By providing robust, scalable, and secure cloud computing services, AWS has fundamentally transformed how businesses operate.

The importance of AWS stretches beyond mere business operations. Its use has become so widespread that AWS proficiency is a hot ticket in the job market, making it a valuable skill for tech professionals to acquire and a vital one for hiring managers to recognize.

In this article, we dive into the world of AWS — its features, advantages, real-world use cases, key skills, and its value in the hiring landscape. Whether you’re a tech professional seeking to bolster your skillset or a hiring manager aiming to future-proof your team, this deep dive into AWS will arm you with the knowledge you need to navigate the world of cloud computing. 

What is AWS?

At its core, Amazon Web Services (AWS) is a comprehensive cloud services platform that provides an array of infrastructure services such as storage, compute power, networking, and databases on demand, available in seconds, with pay-as-you-go pricing. These services are utilized by businesses to scale and grow, without the need to maintain expensive and complex IT infrastructure.

The birth of AWS can be traced back to the early 2000s when Amazon, primarily an e-commerce giant at the time, realized they had developed a deep expertise in operating large-scale, reliable, scalable, distributed IT infrastructure. They understood the pain points of managing such a system and recognized that other businesses could benefit from their expertise. 

In 2006, Amazon launched AWS, providing businesses with a means to access the cloud. Since then, AWS has continually expanded its services to include not just storage and compute power, but also machine learning, artificial intelligence, database management, and Internet of Things (IoT) services, to name a few. Today, AWS is the most widely adopted cloud platform across the globe, serving millions of customers from startups to enterprise-level organizations.

AWS offers over 200 fully-featured services from global data centers. Understanding AWS, its services, and how to leverage the platform is crucial for cloud professionals. With AWS, the possibilities are, quite literally, sky-high. So, let’s explore some key features that make AWS a frontrunner in the cloud services platform arena.

Key AWS Offerings

AWS comes packed with a wide range of features designed to help businesses grow. Here are some of the key ones that have made AWS the go-to cloud services platform:

Compute Power

With AWS, you have access to compute power whenever you need it. Services like Amazon Elastic Compute Cloud (EC2) and Amazon LightSail make it easy to scale up and down quickly and affordably. Take the example of a retail website running a Black Friday sale. With AWS, it can easily scale up its resources to handle the surge in traffic and then scale down when traffic returns to normal, thus ensuring an optimal user experience while maintaining cost efficiency.

Storage & Content Delivery

Amazon Simple Storage Service (S3) is one of the most widely used services of AWS, offering secure, scalable, and durable storage. Amazon S3 allows businesses to collect, store, and analyze data, regardless of its format. Alongside this, Amazon CloudFront, a fast content delivery network (CDN) service, delivers data, videos, and APIs to customers globally with low latency and high transfer speeds.

Database Services

AWS offers a broad range of databases designed for diverse types of applications. Amazon RDS makes it easy to set up, operate, and scale a relational database, while DynamoDB provides a scalable NoSQL database for applications with high throughput needs. For data warehousing, AWS offers Redshift, a fast, scalable data warehouse that makes it simple and cost-effective to analyze all your data.

Networking Services

With services like Amazon Virtual Private Cloud (VPC), AWS allows businesses to create isolated networks within the cloud, offering robust network control over their environment, including selection of their own IP address range, creation of subnets, and configuration of route tables and network gateways.

Management Tools

Managing resources within AWS is made simple with its array of management tools. AWS CloudFormation allows businesses to model their resources and provision them in an orderly and predictable fashion, while AWS CloudWatch provides systemwide visibility into resource utilization and operational health.

Advantages of Using AWS

There’s a reason — or rather several reasons — why AWS has become a de facto choice for businesses of all sizes when it comes to cloud services. Let’s unpack some of the key advantages.

Scalability

One of the primary benefits of AWS is its ability to scale. AWS services are designed to adapt to a business’s usage needs, allowing users to increase or decrease capacity as and when required. Whether it’s a small business anticipating growth or a large corporation dealing with heavy loads, AWS offers the flexibility to scale on demand.

Security

Security is paramount, and AWS doesn’t take it lightly. AWS’s infrastructure is keeps data safe using an end-to-end secure and hardened infrastructure, including physical, operational, and software measures.

Cost-Efficiency

With AWS, businesses can pay for what they use, with no upfront costs or long-term commitments. The pay-as-you-go approach allows businesses to have access to enterprise-level infrastructure at a fraction of the cost. This pricing model has opened doors for many startups and small businesses to implement solutions that were previously out of reach due to cost constraints.

Diversity of Tools

From data warehousing to deployment tools, AWS houses a diverse suite of services that can be used together or independently to meet any business need. This diversity ensures that you can choose the right tool for the job and not be shoehorned into a one-size-fits-all solution.

Global Infrastructure

AWS has data centers spread across multiple regions globally, enabling customers to deploy their applications in various geographic locations with just a few clicks. This global presence translates into lower latency and better user experience for end users.

Key AWS Skills

Behind AWS’ widespread adoption are cloud engineers that build their company’s cloud infrastructure with AWS services. Proficiency in Amazon Web Services (AWS) demands a comprehensive understanding of various domains within the cloud ecosystem.

Computing Services

• Proficiency in Amazon EC2 (Elastic Compute Cloud) for virtual server provisioning.
• Knowledge of AWS Lambda for serverless computing and event-driven architectures.

Storage Services

• Expertise in Amazon S3 (Simple Storage Service) for object storage and data backup.
• Familiarity with Amazon EBS (Elastic Block Store) for persistent block storage.

Database Services

• Skill in managing Amazon RDS (Relational Database Service) for managed relational databases.
• Knowledge of Amazon DynamoDB for NoSQL database management.

Networking and Content Delivery

• Understanding of Amazon VPC (Virtual Private Cloud) for network isolation and security.
• Proficiency in Amazon CloudFront for content delivery and distribution.

Security and Identity

• Familiarity with AWS IAM (Identity and Access Management) for managing user permissions.
• Knowledge of AWS Key Management Service (KMS) for encryption and key management.

Monitoring and Management

• Skill in using Amazon CloudWatch for monitoring resources and generating alerts.
• Understanding of AWS Systems Manager for automating operational tasks.

Automation and Orchestration

• Proficiency in AWS CloudFormation or Terraform for Infrastructure as Code (IaC).
• Knowledge of AWS Step Functions for orchestrating workflows.

DevOps Practices

• Experience with AWS CodePipeline and AWS CodeDeploy for CI/CD.
• Skill in using AWS CodeCommit for version control.

Serverless Architecture

• Expertise in AWS Lambda for building serverless applications.
• Knowledge of Amazon API Gateway for creating RESTful APIs.

Migration and Transfer

• Understanding of AWS Database Migration Service for database migration.
• Familiarity with AWS Snowball for data transfer.

Analytics and Big Data

• Skill in Amazon Redshift for data warehousing.
• Knowledge of Amazon EMR (Elastic MapReduce) for big data processing.

AI and Machine Learning

• Experience with Amazon SageMaker for machine learning model training and deployment.
• Familiarity with Amazon Rekognition for image and video analysis.

Hybrid Cloud Solutions

• Understanding of AWS Direct Connect for establishing dedicated network connections.
• Knowledge of AWS VPN for secure communication between on-premises and cloud resources.

Cost Management

• Proficiency in AWS Cost Explorer for monitoring and optimizing costs.
• Understanding of AWS Budgets for cost control.

The Hiring Landscape for AWS Skills

The ripple effects of AWS’s impact are clearly felt in the hiring market. With the broad adoption of cloud technologies across industries, the demand for AWS skills is soaring. 

The proliferation of AWS has led to a significant increase in demand for professionals proficient in this platform. According to the 2022 Global Knowledge IT Skills and Salary Report, AWS Certified Developer is the second highest-paying certification in North America, garnering an average annual salary of $165,333 and reflecting the high demand for AWS skills. 

The demand for AWS skills extends across many roles. Positions like AWS Solutions Architect, AWS SysOps Administrator, and DevOps Engineer are in high demand. These roles involve designing and deploying AWS systems, managing and operating systems on AWS, and working with technologies for automated deployments, respectively. 

In the face of digital transformation, the importance of cloud computing, and specifically AWS skills, cannot be overstated. For tech professionals, AWS proficiency can open up lucrative opportunities and exciting career paths. For hiring managers, spotting and attracting AWS talent is essential to stay competitive and drive innovation. As the cloud continues to dominate, the AWS wave is one worth riding for both professionals and organizations.

Key Takeaways

Cloud computing has taken center stage, and at the heart of this revolution stands AWS. Its remarkable array of services has democratized technology, enabling businesses of all sizes to innovate, scale, and grow.

AWS’s influence extends beyond business operations; it’s fundamentally altering the tech job market. AWS skills have become increasingly valuable, paving the way for exciting career opportunities for tech professionals and creating a new criterion for hiring managers to seek out.

So, whether you’re a tech professional looking to upskill or a hiring manager seeking to future-proof your team, understanding and embracing AWS is a strategic move. AWS isn’t just a platform; it’s a game-changer, powering the future of business operations, technological innovation, and the ever-evolving tech job market. 

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Cloud computing has become much more than just a buzzword over the last two decades — it represents a seismic shift that has fundamentally transformed the technology industry and the way businesses operate. According to Gartner, the public cloud services market is forecasted to grow 20.7 percent to $591.8 billion in 2023, up from $490.3 billion in 2022. That’s not just a trend — it’s a tech revolution.

With a seemingly endless array of platforms and services, cloud computing is democratizing technology, breaking down barriers to entry, and enabling innovation at an unprecedented scale. From scrappy startups leveraging scalability to Fortune 500 companies streamlining their operations, cloud computing is not just a tool – it’s the new normal.

Yet, despite the sweeping changes it has already brought, cloud computing is not static. It continues to evolve, driven by relentless technological advancement and ever-changing business needs. So where’s it headed next? And what does the future of cloud computing look like? These are not just questions for tech enthusiasts, but crucial considerations for anyone involved in the technology industry — whether you’re a hiring manager scouting for top talent or a professional looking to ride the next big wave.

#1. AI and ML Become More Embedded in Cloud Computing

The synergy between machine learning (ML) and cloud computing is more than a marriage of convenience. It’s a powerful partnership that’s redefining what’s possible in AI.

AI and ML, known for their data-hungry nature, are no longer confined to high-powered research labs and enterprises with the on-site resources to feed them. Today, these technologies are accessible to many, thanks to the vast data processing capabilities and virtually limitless storage offered by cloud computing. According to a recent report by Red Hat, 66% of organizations deploy their AI and ML models using a hybrid cloud strategy, with another 30% of companies using only cloud infrastructure to power their models. 

This fusion has brought us AI-powered chatbots that offer personalized customer service, real-time fraud detection systems that safeguard our online transactions, and advanced predictive models that provide invaluable business insights, to name a few.

Cloud-based AI and ML are also enhancing automation within cloud systems themselves. For instance, AI can be used to automate routine administrative tasks, such as resource provisioning and load balancing, reducing human error and improving operational efficiency. 

Furthermore, AI and ML are pushing the boundaries in cloud security. Machine learning algorithms can be trained to detect unusual behavior or anomalies in network traffic, flagging potential threats before they become full-blown security incidents. According to Capgemini, 69% of organizations believe that they can’t respond to critical threats without AI.

In short, AI and ML are not just adding bells and whistles to cloud computing — they’re deeply woven into the fabric of this technology, pushing its capabilities to new heights. The potential is enormous, and we’re only scratching the surface of this game-changing trend. 

#2. Investment in Cloud Security Becomes a Must

As cloud computing becomes a dominant force in the IT landscape, securing these cloud platforms is becoming a paramount concern. Per a recent report, the cloud security market size is projected to grow from $40.8 billion in 2022 to $77.5 billion by 2026, almost doubling in just four years. This trend clearly underscores the growing focus and investment on cloud security by organizations of all sizes and industries.

Cloud security is not a single monolithic entity though; rather, it is a collection of multiple security protocols, tools, and strategies designed to protect data, applications, and the infrastructure of cloud computing. It covers areas like data privacy, compliance, identity and access management, and protection against threats like data breaches, DDoS attacks, and malware.

One of the key reasons behind this increased investment is the rise in sophisticated cyber threats, which increased by 38 percent in 2022. As technology advances, so does the cunning and capability of cybercriminals. A single security breach can lead to significant financial loss and damage to an organization’s reputation, making it crucial for organizations to stay one step ahead.

Further, the shift toward remote working has amplified the need for robust cloud security. With employees accessing sensitive company data from various locations and often on personal devices, the potential for security vulnerabilities has increased. In this context, cloud security tools and protocols play a critical role in safeguarding data and maintaining business continuity.

Moreover, regulatory requirements are also driving investment in cloud security. Regulations like GDPR in Europe and CCPA in California demand stringent data security measures from organizations, pushing them to invest more in securing their cloud platforms.

Looking ahead, expect cloud security to remain a top priority for organizations in 2023 and beyond. As more data and processes migrate to the cloud, we’ll see a continued focus on developing advanced security strategies, tools, and best practices to protect these virtual environments.

#3. Multi-Cloud and Hybrid Strategies Become Standard

In the early days of cloud computing, many organizations found themselves tied to a single cloud provider, often finding themselves locked into their services. As the industry evolved, these organizations came to the realization that a “one-size-fits-all” approach did not cater to the diverse needs of their businesses. This realization gave birth to multi-cloud and hybrid cloud strategies, a trend that is gathering speed in 2023.

According to the Flexera 2023 State of the Cloud Report, 87% of enterprises have a multi-cloud strategy, while 72% have a hybrid cloud strategy. But what’s driving this shift toward using multiple cloud vendors and a blend of private and public clouds?

One key factor is avoiding vendor lock-in. By distributing workloads across multiple providers, companies gain more flexibility and reduce the risk of being too reliant on a single provider. It also allows companies to leverage the best features and services from different providers, creating an IT environment tailored to their specific needs.

Moreover, multi-cloud and hybrid strategies can also enhance operational resilience. By not having all their eggs in one basket, companies can mitigate the risk of a single point of failure. If there’s a service disruption in one cloud, they can ensure business continuity by relying on their other cloud environments.

Container technologies like Kubernetes and Docker play a pivotal role in realizing the benefits of multi-cloud and hybrid strategies. Kubernetes, an open-source container orchestration tool, helps manage workloads across multiple clouds, ensuring they interact seamlessly. Docker, on the other hand, simplifies the creation and deployment of applications within containers, making them portable across different cloud environments.

These tools support the implementation of a multi-cloud or hybrid cloud strategy by making it easier to move workloads across different clouds and ensuring they operate consistently, regardless of the underlying infrastructure.

In 2023, the shift towards multi-cloud and hybrid cloud strategies is expected to continue. As businesses strive for agility, operational resilience, and best-in-class services, a diversified approach to cloud computing seems to be the way forward.

#4. Industry-Specific Cloud Adoption Grows

Every industry has its unique needs and challenges, and the one-size-fits-all approach of the early cloud days is evolving to accommodate these specifics. In 2023, one of the significant cloud computing trends is the rise of industry-specific cloud solutions, often termed as industry clouds. According to a recent Gartner survey among North American and European-based enterprises, nearly 40% of respondents had started the adoption of industry cloud platforms, with another 15% in pilots and an additional 15% considering deployment by 2026.

But what exactly are industry clouds, and why are they gaining traction? Industry clouds are cloud services and solutions tailored to the needs of a specific industry — like healthcare, finance, manufacturing, or retail. These clouds come equipped with industry-specific features and compliance measures, making them ready-to-use platforms for businesses within that industry.

For instance, cloud solutions designed for the healthcare industry — such as Microsoft Cloud for Healthcare and CareCloud — come with features to support electronic health records, telemedicine, and medical imaging. These platforms also comply with healthcare regulations like HIPAA, making it easier for healthcare providers to adopt and use these solutions without fretting over compliance issues.

This industry-specific approach has multiple benefits. Firstly, it reduces the need for extensive customization — businesses get a platform that is already attuned to their needs, helping them get started faster. Secondly, it reduces the compliance burden, especially in heavily regulated industries like healthcare and finance. Finally, it brings industry-specific innovations to the table — like AI-powered risk assessments in finance or remote patient monitoring in healthcare, enhancing the capabilities of businesses within those industries.

The growing adoption of industry clouds is a testament to the maturing cloud computing market, where customization and specialization are playing an increasingly important role. This trend not only brings the benefits of cloud computing to more businesses but also fosters innovation within industries, making it a trend to watch in 2023.

#5. Cloud-Native Architecture Matures

As more businesses embrace the cloud, there’s a growing trend toward building applications that are native to this environment, known as cloud-native architecture. According to the Cloud Native Computing Foundation’s 2022 survey, 44% of respondents stated they’re already using containers for nearly all applications and business segments and another 35% said they use containers for at least a few production applications. Given that containers are often a key component of cloud-native applications, these numbers indicate a substantial shift toward cloud-native technologies.

But why the surge in interest? Cloud-native architecture provides several key advantages over traditional application development. 

Firstly, it offers exceptional scalability. Cloud-native applications are built around microservices, which are individual, loosely coupled services that make up a larger application. This means individual components can be scaled up or down based on demand, allowing for efficient use of resources.

Secondly, cloud-native architecture is designed with resilience in mind. Given the distributed nature of microservices, if one service fails, it does not bring down the entire application. This design aids in achieving higher application uptime and a better user experience.

Thirdly, it fosters faster innovation and reduces time to market. With microservices, teams can work on different services independently, making updates or adding new features without waiting for a full application release.

The rise of cloud-native architecture is intertwined with open source and serverless computing. Open-source projects like Kubernetes and Docker have been instrumental in accelerating the adoption of cloud-native architectures, providing the necessary tools to manage and orchestrate containers.

On the other hand, serverless computing takes the cloud-native approach a step further by abstracting away even the infrastructure layer. Developers just need to write code, and the cloud provider takes care of the rest — from managing servers to scaling. This allows developers to focus solely on coding and delivering value, making serverless computing a significant player in the rise of cloud-native.

As we navigate through 2023, we can expect to see a continued surge in cloud-native architecture as businesses strive to make the most of their cloud investments. With its promise of scalability, resilience, and speed, cloud-native is the new frontier in cloud computing.

#6. Quantum Computing Becomes Democratized

If you’ve been keeping an eye on technology trends, you’ve likely heard whispers — and perhaps a few loud proclamations — about quantum computing. This exciting field promises to redefine what’s possible in computing, solving complex problems that would take traditional computers thousands of years to crack.

But quantum computers are expensive and challenging to maintain, putting them out of reach for most businesses. That’s where cloud computing comes into play. The intersection of quantum computing and cloud services has emerged as a significant trend in 2023, democratizing access to quantum computing capabilities. A report by MarketsandMarkets projected the global cloud-based quantum computing services market to grow from an estimated $798 million in 2023 to $4.06 billion by 2028.

Several tech giants, including IBM, Google, and Microsoft, offer cloud-based quantum computing services, allowing businesses to run quantum algorithms without owning a quantum computer. These cloud-based quantum platforms also provide developers with the tools to experiment with quantum programming and develop quantum software applications.

But quantum computing in the cloud isn’t just about granting access to quantum machines. It’s also about integrating quantum capabilities with classical computing resources. Hybrid quantum-classical algorithms, where a classical computer and a quantum computer work in tandem, offer exciting possibilities. For instance, a quantum processor could handle computationally intensive tasks, while a classical computer manages other parts of the algorithm, optimizing the use of resources.

The trend of quantum computing in the cloud holds enormous potential. While it’s still in the nascent stages, with quantum technology becoming more mature and accessible, businesses of all sizes will start to explore quantum solutions for their most complex problems.

This integration of quantum computing capabilities into the cloud environment signifies a significant leap forward in computing and is a trend worth watching in 2023 and beyond. It might not be long before quantum cloud services become a standard offering alongside the familiar classical cloud resources.

#7. Cloud FinOps Addresses Rising Costs

As organizations scale their cloud operations, managing and optimizing cloud costs become increasingly complex yet critical tasks. This is where cloud financial management, or cloud FinOps, comes into play. In a survey of over 1,000 IT decision makers, HashiCorp-Forrester reported that 94% of respondents said their organizations had notable, avoidable cloud expenses due to a combination of factors such as underused and overprovisioned resources, and a lack of skills to utilize cloud infrastructure.

Cloud FinOps is a practice designed to bring financial accountability to the variable spend model of the cloud, enabling organizations to get the most business value out of each cloud dollar spent. In essence, it’s all about understanding and controlling cloud costs while maximizing the benefits.

Cost optimization is the primary driver behind FinOps. Unlike traditional IT purchasing, where costs are typically fixed and capital-based, cloud costs are operational and can fluctuate based on usage. This means that poorly managed resources can lead to cost overruns and wasted spend. 

FinOps practices help organizations forecast and track cloud spending, allocate costs to the right departments or projects, and identify opportunities for cost savings. This might involve rightsizing resources, selecting the right pricing models (like choosing between on-demand, reserved, or spot instances), or identifying and eliminating underused or orphaned resources.

Importantly, FinOps is not just a finance or IT function — it’s a cross-functional practice that brings together technology, business, and finance teams to make collaborative, data-driven decisions about cloud usage and spend. 

As businesses rely more on the cloud, cloud FinOps will continue to grow in importance. In fact, the FinOps Foundation research indicates that 60 to 80 percent of organizations are building FinOps teams.

Going forward in 2023, expect cloud FinOps to become a standard practice for organizations seeking to align their cloud investments with business objectives. As the saying goes, “You can’t manage what you can’t measure,” and cloud FinOps provides the tools and practices needed to measure — and hence manage — cloud costs effectively.

#8. Edge Computing Complements the Cloud

If you think the story of cloud computing is all about centralized data centers, think again. One of the most exciting cloud computing trends in 2023 is the rise of edge computing, a market that’s expected to reach an estimated $74.8 billion by 2028.

So, what is edge computing, and why is it so crucial to the future of cloud computing? 

Edge computing is a model where computation is performed close to the data source, i.e., at the “edge” of the network, instead of being sent to a centralized cloud-based data center. This drastically reduces latency and bandwidth usage, as less data needs to be sent over the network.

Consider a self-driving car. It generates enormous amounts of data that need to be processed in real time to make split-second decisions. Sending this data to a cloud data center and waiting for a response isn’t practical due to latency. With edge computing, this data can be processed on the vehicle itself or a nearby edge server, enabling real-time decision making.

But this doesn’t mean edge computing is replacing cloud computing. Far from it. Instead, edge computing complements cloud computing, forming a powerful combination that brings together the best of both worlds. The edge can handle time-sensitive data, while the cloud takes care of large-scale computation, storage, and less time-sensitive tasks.

The rise of IoT devices and the rollout of 5G are key drivers of this trend. As these devices proliferate and 5G reduces network latency, edge computing becomes increasingly viable and necessary.

In 2023, expect to see more businesses integrating edge computing into their cloud strategies. This combination of localized data processing with the computational power of the cloud paves the way for innovative applications, from autonomous vehicles to smart factories, reshaping the future of technology and business.

A Dynamic Cloud on the Horizon

In 2023, it’s clear that the cloud computing landscape is experiencing dynamic change and growth. The trends we’ve explored reflect a shift toward increased automation, resilience, cost-effectiveness, and versatility in the cloud. 

From the pervasive influence of AI and machine learning to the proliferation of multi-cloud and cloud-native strategies supported by powerful tools like Kubernetes and Docker, organizations are getting more sophisticated and efficient in how they use the cloud. 

These trends illustrate a cloud computing environment that’s maturing, diversifying, and becoming even more integral to our digital economy. As businesses, developers, and IT professionals, keeping a finger on the pulse of these trends is critical to harnessing the power of the cloud and driving innovation.

This article was written with the help of AI. Can you tell which parts?

The post Top 8 Cloud Computing Trends in 2023 appeared first on HackerRank Blog.

The cloud computing industry has experienced tremendous growth over the past decade, with businesses of all sizes embracing the cloud for its flexibility, scalability, and cost-effectiveness. The availability of cloud-based solutions has enabled companies to operate in a more agile and efficient manner, allowing them to focus on innovation and growth rather than managing their own infrastructure. As a result, the demand for skilled cloud engineers has skyrocketed, with companies eagerly seeking individuals who can design, implement, and manage cloud-based solutions that meet their unique needs.

The pace of innovation in the cloud shows no signs of slowing down either, with new tools and services being introduced on a regular basis. In fact, Gartner forecasts worldwide public cloud end-user spending to reach nearly $600 billion in 2023. As more companies shift to the cloud, the demand for cloud engineering skills continues to rise, making it crucial for tech professionals to stay up-to-date with the latest trends and technologies in the field. In this blog post, we’ll explore some of the most important cloud computing skills that will be in high demand in 2023, providing insights for both hiring managers and tech professionals alike.

Cloud Security

With more and more data being stored in the cloud, security is becoming a top priority for organizations, making this one of the most critical skills for cloud engineers to possess in 2023. As companies continue to move their operations to the cloud, they must ensure that their data and systems are secure from threats such as hacking, data breaches, and cyber attacks. Cloud security encompasses a range of best practices, technologies, and principles that are designed to protect cloud-based assets from these types of threats.

Key cloud security principles include:

• Identity and access management, which ensures that access is only granted to authorized users
• Data encryption, which is the process of encoding sensitive data to protect it from unauthorized access
• Network security, which involves securing the communication channels between cloud-based assets and users; 
• Threat management, which allows cloud engineers to monitor and respond to potential threats to cloud-based assets, such as malware or denial-of-service attacks.

Cloud engineers have a variety of tools and technologies at their disposal to manage security. This includes firewalls, intrusion detection systems, and security information and event management (SIEM) systems. Combined, these technologies help engineers prevent unauthorized access to cloud-based assets, monitor network traffic to identify potential threats, and collect and analyze security-related data from multiple sources, providing a comprehensive view of potential security issues.

Cloud Architecture

Cloud architecture, which refers to the design and structure of cloud-based systems, components, and services, is another essential skill for cloud engineers to have in 2023. 

Some of the key principles of cloud architecture include scalability, availability, reliability, and performance. These principles ensure that the cloud system is able to remain operational in the event of failures or disruptions, handle workloads efficiently, perform consistently over time, and handle increasing amounts of traffic or data without compromising performance.

To achieve these key principles, cloud architects design systems that make use of the appropriate cloud-based services and resources. These might include compute resources like virtual machines, storage resources like object storage or block storage, or networking resources like virtual private clouds or load balancers. Cloud architects must also ensure that these resources are configured and optimized to meet the needs of the system they are designing.

Some of the key cloud architecture technologies and tools cloud engineers should be familiar with include:

• Infrastructure as code (IaC) tools, like Terraform
• Containerization platforms, particularly platforms like Docker or Kubernetes
• Serverless computing services, which allow developers to focus on writing code without worrying about underlying infrastructure.

Automation and Orchestration

As more companies move to the cloud, the complexity of cloud-based systems is increasing. This means that there are more moving parts to manage and deploy, which can be time-consuming and error-prone if done manually. Automation and orchestration skills are critical for managing these complexities. 

Cloud automation is the process of automating the deployment, scaling, and management of cloud-based systems. With cloud automation, tasks that would normally require manual intervention, such as provisioning servers or deploying code, can be automated, saving time and reducing the risk of human error.

Cloud orchestration takes this one step further by automating the management and coordination of complex cloud-based systems. With cloud orchestration, engineers can manage and coordinate the interactions between different cloud-based services and applications, making it easier to deploy and manage complex systems.

To become proficient in cloud automation and orchestration, engineers should have experience with scripting languages like Python or PowerShell, as well as knowledge of configuration management tools like Ansible or Puppet. Familiarity with cloud-based orchestration tools like Kubernetes or Docker Swarm is also important.

Cloud Cost Optimization

As companies move to the cloud, they’re realizing the benefits of cost savings and scalability. However, as cloud usage increases, so do the costs. Cloud computing can be expensive, and if not managed properly, costs can quickly spiral out of control.

That’s where cloud cost optimization comes in. It’s the process of optimizing cloud costs to ensure that organizations get the most value out of their cloud investments. With cloud cost optimization, engineers can identify areas where costs can be reduced or eliminated, while still ensuring that cloud-based systems are meeting the needs of the organization.

One important cost optimization principle is the use of reserved instances or committed use contracts. These allow organizations to commit to a certain amount of cloud usage over a period of time, which can result in significant cost savings.

Another important principle is the use of autoscaling. Autoscaling allows organizations to automatically increase or decrease resources based on demand, ensuring that they’re only paying for what they need. This can result in significant cost savings, especially during periods of low demand.

Engineers should also be familiar with cloud cost management tools, such as AWS Cost Explorer or Google Cloud Billing. These tools can help engineers identify areas where costs can be reduced or eliminated, and provide insights into cloud usage patterns and trends.

To become proficient in cloud cost optimization, engineers should have a deep understanding of cloud usage patterns and trends, as well as a strong understanding of cloud pricing models and cost management tools. Familiarity with scripting languages like Python or PowerShell can also be helpful with optimizing costs.

Cloud Migration

Cloud migration is the process of moving data, applications, and other business elements from an organization’s on-premises infrastructure to the cloud. It involves several phases, including assessment, planning, execution, and optimization, and it requires an in-depth understanding of both the current infrastructure and the target cloud environment.

One of the most critical cloud migration skills is the ability to assess the current infrastructure and determine which applications and workloads are best suited for migration. The assessment phase involves analyzing various factors, such as data security requirements, regulatory compliance, and performance metrics. A cloud engineer with migration skills can also identify any potential issues that may arise during migration, such as compatibility issues, data loss, and service disruptions.

Once the assessment phase is complete, the cloud engineer can begin the planning phase. This phase involves developing a detailed migration plan that includes timelines, resource requirements, and a risk management strategy. Cloud engineers should be able to help organizations choose the right cloud provider, select the appropriate migration tools, and develop a strategy for testing and validating the migration plan.

The execution phase is where the actual migration takes place. Cloud engineers oversee the migration process, monitor progress, and troubleshoot any issues that arise. They should also provide regular updates to stakeholders, manage any change requests, and ensure that the migration is completed on time and within budget.

Cloud Analytics

Cloud analytics is an important skill for cloud engineers because it allows them to extract valuable insights and knowledge from the data collected by cloud-based applications and systems. With the ability to harness the power of data, organizations can optimize their operations, make data-driven decisions, and gain a competitive advantage.

To put it simply, cloud analytics refers to the process of collecting, analyzing, and interpreting data generated by cloud-based systems. This data can include user behavior, performance metrics, and usage patterns, among other things. With cloud analytics, organizations can use this data to monitor their systems, detect issues and anomalies, and identify opportunities for improvement.

Some of the key cloud analytics tools and technologies that cloud engineers should be familiar with include cloud-based data warehouses such as Amazon Redshift and Google BigQuery, data visualization tools such as Tableau and PowerBI, cloud-based machine learning tools such as Amazon SageMaker and Google Cloud AI Platform, big data technologies such as Hadoop and Spark, as well as machine learning and AI.

In addition to these tools and technologies, cloud engineers should also be familiar with data governance and privacy regulations. Ensuring that data is secure, compliant, and properly managed is critical in the cloud environment and an important piece of the analytics puzzle.

Collaboration and Communication

Collaboration and communication are crucial skills for cloud engineers. The ability to work with other team members, communicate ideas effectively, and provide feedback can make or break a project. Cloud engineers need to be able to explain complex technical issues to technical and non-technical stakeholders, work with cross-functional teams, and coordinate with various departments to ensure that projects are delivered on time and within budget. This requires effective communication skills, the ability to listen actively, and the capacity to work in a team environment.

In addition, cloud engineers need to be skilled at providing feedback to other team members. This feedback may include suggesting improvements, identifying issues, or proposing new ideas. The ability to provide constructive feedback in a way that is both clear and non-confrontational is an essential component of collaboration.

Effective communication skills are also critical when working with non-technical stakeholders, such as business leaders, customers, and vendors. Cloud engineers must be able to explain complex technical concepts in a way that is understandable to these stakeholders. This requires the ability to communicate in plain language, present information clearly, and listen actively.

Key Takeaways

As you can see, cloud computing skills are becoming increasingly important for tech professionals as the demand for cloud services continues to grow. To succeed in this field, cloud engineers need to have a diverse set of skills that go beyond just technical expertise. 

If you’re a hiring manager, make sure to look for candidates who possess these skills, as they will be the ones who can help your organization fully harness the power of the cloud. And if you’re a tech professional interested in advancing your career in cloud computing, now is the time to start building your skills in these areas.

To learn more about the specific skills that are in demand in the cloud computing industry, check out HackerRank’s roles directory.

The post The 7 Most Important Cloud Engineering Skills in 2023 appeared first on HackerRank Blog.

In today’s technology landscape, cloud computing has become a ubiquitous presence. Roughly 94 percent of organizations use the cloud in some capacity, ranging from basic storage solutions to more complex platforms for application development, analytics, and artificial intelligence and machine learning initiatives. 

As workforces become increasingly distributed, and companies more keen to invest in these kinds of efforts, the demand for fast, flexible and accessible cloud services is only expected to grow. This is because technologies like AI and ML require massive amounts of computing power, storage and connectivity, which are often difficult to manage with traditional on-premise solutions. The cloud provides a scalable and cost-effective solution to this problem, enabling companies to quickly and easily deploy and manage the resources they need to stay ahead of the competition.

But building and maintaining these systems is a complex task that requires specialized knowledge and skills — and the ability to adapt to an industry that’s constantly evolving.  

That’s where cloud architects come in. 

What Are a Cloud Architect’s Responsibilities?

Cloud architects are responsible for designing and managing the cloud-based infrastructure and applications that make up a company’s computing system. They work to ensure that these systems operate efficiently and securely while meeting the specific needs of the organization. In essence, cloud architects are the conductors of the cloud, overseeing and coordinating all the moving parts to create a harmonious and effective system.

Designing and Implementing Cloud Infrastructure 

Cloud architects are responsible for designing, implementing, and managing the cloud infrastructure that meets the requirements of their organization. This includes selecting the appropriate cloud service provider, defining the cloud architecture, and setting up the necessary cloud resources.

Developing Cloud Strategies 

Cloud architects need to understand the business requirements and develop cloud strategies that align with them. They need to ensure that the cloud infrastructure is scalable, reliable , and cost-effective.

Managing Cloud Security 

Another important aspect of the cloud architect’s role is ensuring the security of the cloud infrastructure. This includes implementing security policies, monitoring the cloud infrastructure for security threats, and implementing security controls to mitigate risks.

Managing Cloud Operations

Cloud architects are also responsible for managing the day-to-day operations of the cloud infrastructure. This includes monitoring the performance of the cloud infrastructure, optimizing the infrastructure for efficiency, and ensuring that the infrastructure is highly available, which allows the system to keep functioning, even when some components fail.

Collaborating With Stakeholders

Cloud architects work closely with stakeholders to understand their requirements and ensure that the cloud infrastructure supports their goals. They need to communicate effectively with both technical and non-technical stakeholders and make sure everyone is on the same page.

What Kinds of Companies Hire Cloud Architects?

Cloud architects are in high demand, and organizations in various industries are searching for them. Many technology-focused companies like Amazon, Microsoft and Google require cloud architects to manage their cloud-based infrastructure. However, as cloud computing has increasingly replaced or supplemented on-premise data centers, companies outside the tech industry are now hiring cloud architects in greater and greater numbers, too. 

Healthcare organizations use cloud computing to store electronic health records and conduct medical research. Financial institutions utilize the cloud for online banking and financial analysis. Retail companies use cloud computing to store customer data and conduct e-commerce transactions. Essentially, any organization that needs to store, process or analyze data online can benefit from hiring a cloud architect.

Types of Cloud Architect Positions

There are many types of cloud architect roles, which can differ depending on experience, education and company size.

At the entry-level, a cloud architect may start as a Junior Cloud Architect or Cloud Solutions Architect, working on developing and testing cloud-based solutions. As they gain experience, cloud architects may move into senior-level roles such as: 

• Senior Cloud Architect
• Cloud Infrastructure Architect
• Cloud Security Architect
• Cloud Integration Architect

At the highest levels of an organization, cloud architects may take on leadership roles such as Chief Cloud Architect, Cloud Technical Director or Cloud Strategy Lead, where they are responsible for setting the overall cloud strategy and guiding the direction of the organization’s cloud-based solutions.

Cloud architects must stay up-to-date with the latest cloud technologies and best practices. They may even choose to specialize in a particular cloud platform such as AWS, Azure or Google Cloud, which can provide a competitive edge in the job market. 

Career outlook and earning potential for cloud architects can vary depending on factors such as industry, company size and experience. However, as more organizations move their operations to the cloud, demand for skilled cloud architects is expected to remain high.

Skills Needed to Become a Cloud Architect

Technical Skills

Technical skills are crucial for cloud architects to design, implement and manage complex cloud-based solutions. Some of the essential technical skills needed to be a successful cloud architect include:

• Cloud Computing Platforms: Cloud architects must have expertise in cloud platforms such as AWS, Azure and Google Cloud. They should be able to work with cloud-based services such as compute, storage, and database, and know how to select the right service to meet the business needs.
• Infrastructure-as-Code (IaC): IaC is a method of managing and provisioning infrastructure through code. Cloud architects should be familiar with tools like Terraform, AWS CloudFormation, and Azure Resource Manager to create and manage infrastructure and resources on the cloud.
• Automation: Automation is essential for managing cloud-based infrastructure at scale. Cloud architects should have experience with automation tools like Ansible, Chef or Puppet to automate provisioning, configuration and deployment.
• Networking: Cloud architects should have knowledge of cloud networking concepts such as virtual private clouds (VPCs), subnets, routing tables, load balancing, and security groups. They should be able to design and implement secure and scalable network architectures.
• Security: Cloud architects must have a strong understanding of cloud security concepts such as identity and access management (IAM), encryption, and data protection. They should be able to design and implement secure cloud architectures that meet compliance and regulatory requirements.
• Monitoring and Logging: Cloud architects should be able to set up and configure monitoring and logging tools such as CloudWatch, Azure Monitor, and Stackdriver to monitor cloud-based infrastructure and services. They should also be able to set up alerts and notifications to detect and respond to issues quickly.

Soft Skills

But technical skills aren’t the only competencies a cloud architect needs to succeed. On the soft skills side, a cloud architect should possess strong communication skills, problem-solving skills and the ability to work collaboratively with others. Collaboration is particularly important, as the role entails working closely with cross-functional teams, including developers, project managers and IT operations teams, to ensure successful implementation of cloud solutions. In addition, cloud architects need a strong understanding of business needs in order to design cloud-based solutions that actually meet them.

Certifications

Though not necessary, certifications can be a great way for cloud architects to validate their skills and accelerate career advancement. There are several cloud computing certifications available for cloud architects, offered by various cloud service providers and independent organizations. Some of the popular cloud certifications include:

• AWS Certified Solutions Architect – Associate/Professional: Offered by Amazon Web Services, these certifications validate the skills and knowledge needed to design and deploy scalable, fault-tolerant, and highly available systems on AWS.
• Microsoft Certified: Azure Solutions Architect Expert: This certification validates the skills and knowledge needed to design solutions that run on Azure, including compute, storage, networking and security — services that make up the building blocks of cloud architecture.
• Google Cloud Certified – Professional Cloud Architect: Offered by Google Cloud, this certification validates the skills and knowledge needed to design, develop and manage solutions on Google Cloud Platform.
• Certified OpenStack Administrator (COA): Offered by the OpenStack Foundation, this certification validates the skills and knowledge needed to operate and manage an OpenStack cloud infrastructure.
• CompTIA Cloud+: This vendor-neutral certification validates the skills and knowledge needed to understand cloud computing concepts, architectures and security, and to design and deploy cloud-based solutions.

As the world of cloud computing continues to evolve and become more sophisticated, so too will the skills cloud architects need. Whether you’re looking to hire great tech talent or land your next role, it’s important to have a solid understanding of the expertise and experience required for the job. Explore HackerRank’s roles directory to uncover key skills for a variety of technical roles and gain access to a library of resources designed to keep you up to date on the ever-changing tech hiring landscape.

The post What Does a Cloud Architect Do? Role Overview & Skill Expectations appeared first on HackerRank Blog.

Despite being less than two decades old, DevOps plays a vital role in the software development industry. Today, 77% of organizations rely on DevOps to deploy software. Unsurprisingly, this has led to DevOps engineer becoming the seventh most in-demand job in the world. In this post, we break down the statistics, job requirements, and responsibilities of a career in DevOps engineering.

What Is DevOps?

DevOps balances software development and IT operations to support continuous integration and continuous delivery (CI/CD). It’s a methodology with a goal to keep an entire organization working together seamlessly, with agile processes and systems. DevOps allows businesses to create and release updates to their services and products faster than traditional development models. When done well, this means you can deploy code multiple times daily.

What Does a DevOps Engineer Do?

A DevOps engineer is responsible for managing and maintaining code, application maintenance, as well as application management. They work with a variety of experts in different departments to coordinate the design, development, testing, release, and lifecycle management of software and applications.

A DevOps engineer is responsible for any number of tasks related to:

• Project management
• Writing, editing, and coordinating code and review
• System performance testing and maintenance
• Prototyping features and solutions
• Server administration

What Kinds of Companies Hire DevOps Engineers?

Any company that creates software or applications needs DevOps engineers to manage their operations. With companies in every industry becoming increasingly driven by technology, the demand and opportunities for professionals with this skill set continues to grow. The top sectors employing DevOps engineers include:

• Fortune 500: 29%
• Technology: 17%
• Finance: 12%
• Retail: 8%
• Professional Services: 6%
• Telecommunication: 4%
• Manufacturing: 4%
• Insurance: 4%
• Healthcare: 3%

Types of DevOps Engineer Positions

The titles DevOps engineers hold vary drastically, depending on their experience, education, and company. At the beginning of their career, a DevOps engineer will start out with an entry-level role, like junior DevOps engineer or DevOps engineer I. A new DevOps engineer usually works in one of these roles for one to three years.

From there, they’ll have the opportunity to move into more senior-level and specialized roles with hands-on engineering experience. DevOps engineering job titles include: 

• DevOps software developer
• DevOps engineer
• DevOps evangelist
• Automation architect
• Cloud DevOps engineer
• Senior DevOps engineer

While they spend several years honing their skills, their responsibilities expand to include taking ownership of projects, working independently in a team environment, and mentoring project team members.

After gaining more experience, a DevOps engineer often faces a crossroads in their career having to choose between a few paths. 

The first path is to pivot into people and team management functions. Hiring, mentoring, resource planning and allocation, strategy, and operations become a larger component of the responsibilities of DevOps professionals pursuing this career path. At the higher levels of an organization, these job functions might include:

• DevOps engineering manager
• Director of DevOps
• Chief Information Officer (CIO)
• Chief Technology Officer (CTO)

The second possible career path is to continue as an individual contributor. Many DevOps engineers opt to continue their careers as individual contributors, enjoying equally fulfilling careers and developing deeper technical expertise in various languages and frameworks.

The third possible career path is to transition out of DevOps into a related field, such as software development, business analysis, or product management. Because the responsibilities of DevOps intersect with multiple technical disciplines, DevOps engineers are well-positioned to transition to a career in a different field that interests them.

Salary Comparisons and Job Outlook

Estimates of the average annual salary for a DevOps engineer range from $99,527 to $128,387. An entry-level DevOps engineer can earn an average salary of $67,000 while a DevOps engineer later in their career (over 20 years of experience) can average $143,000 annually. This doesn’t factor in bonuses, stock options, and other cash incentives that can add to total compensation.

While we don’t have data on the growth rate of DevOps engineers specifically, the U.S. Bureau of Labor Statistics does include this role in its overall data for software developers. Software developer employment is projected to grow 25 percent through 2031 — more than triple the average for all occupations.

Requirements to Become a DevOps Engineer

Technical Skills

DevOps engineers use a range of programming languages to deliver software. These include, to name a few:

• Python
• Java
• JavaScript
• Go
• PHP
• PERL
• Ruby
• TypeScript

A core requirement of DevOps is expertise in the technologies offered by cloud-hosting providers. These include, to name a few:

• AWS
• Azure
• GCP
• IBM Cloud
• Oracle Cloud

Typically, DevOps engineers are expected to have familiarity with a wide range of development tools, including:

• CI/CD (Bamboo, GitLap, Buddy)
• Virtualization (Kubernetes, Docker)
• Version control (examples: GitHub, Jenkins)
• Monitoring and analytics (Nagios, Prometheus, Zabbix)
• Deployment automation (Red Hat Ansible)
• Cloud computing (Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure)
• Network protocols (TCP, UDP)
• Technical support

DevOps is a constantly evolving field, so it’s important to do research specific to the industries and roles you’re applying to or hiring for to understand specific technical competencies.

Soft Skills

Technical competency alone isn’t enough to succeed in a DevOps engineering role. Analytical, mathematical, and problem-solving skills are a must in any technical job. And in a digital-only or remote first environment, soft skills are even more critical.

Employers may prefer engineers with strong soft skills, such as:

• Problem solving
• Communication
• Project management
• Time management
• Creativity

Experience

After technical skills, the most important qualification for DevOps engineers is experience. On-the-job experience and training is a critical requirement for many employers.

Then, there’s the question of education. The education requirements for DevOps positions vary widely. In the U.S., the vast majority of DevOps engineers have a college education, with 75% having a bachelor’s degree, and 20% having a master’s. 

Many companies still require developers to have a four-year degree. While hiring developers, it’s likely that many of them will have a degree. However, competition for skilled software engineers is high, and it’s not uncommon for job openings with a degree requirement to go unfilled. Ultimately, employers that prioritize real-world skills over pedigree gain access to a larger volume of skilled DevOps talent.

Resources for Hiring DevOps Engineers

Developer Hiring Solutions

The Ultimate Hiring Guide to Developer Skills & Roles

[Checklist] How to Hire the Right DevOps Talent for Your Company

The post What Does a DevOps Engineer Do? Job Overview and Skill Expectations appeared first on HackerRank Blog.

In 2021, the size of the global cloud computing market was valued at $445.3 billion. And it’s expected to grow to $947.3 billion by 2026. That’s a blistering growth rate of 112.7 percent in just five years.

However, the tech industry is facing a huge deficit in the number of skilled cloud engineers available to build this growing industry. If left unchecked, this hiring gap could hinder the growth and innovation of the cloud computing industry writ large. 

In this post, we’ll break down the statistics, job requirements, and responsibilities of a career in cloud engineering.

Overview of the Duties of a Cloud Engineer

Companies of every size and industry are racing to the cloud. Cloud computing is a service that provides on-demand access to computer system resources without direct management or ownership by the party using the service. These services include computing power, data storage, platforms, infrastructure, and software.

Cloud engineers are IT professionals responsible for a company’s cloud computing infrastructure, including design, implementation, maintenance, and support.

Cloud engineers will work in a variety of cloud environments, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

On a more technical level, the core job responsibilities of cloud engineers include:

• Writing highly scalable, testable code
• Building cloud environments on cloud infrastructure platforms
• Configuring cloud infrastructure components including networking and security services
• Discovering and fixing programming bugs
• Presenting and demonstrating features to internal and external stakeholders
• Keeping up-to-date with advancements in technology
• Working in an agile environment

What Kinds of Companies Hire Cloud Engineers?

Any company that’s looking to build cloud infrastructure or migrate their existing systems to the cloud will need to hire cloud engineers. As companies of every size transition to the cloud, the industries that cloud engineers work in continue to expand.

This trend was already well underway, but it was dramatically accelerated by the COVID-19 pandemic and prolonged global lockdowns. Overnight, a majority of the world’s work, education, and entertainment shifted online, leading to a dramatic spike in the demand for platforms, databases, and technologies supported by the cloud. This has left many companies struggling to find the cloud engineers they need to scale in a remote-first world. 

Retail, entertainment, software, consulting, financial services, defense, education, fintech, telecommunications, healthcare — the demand (and opportunity) for cloud engineering is nearly endless. 

In addition to the companies hiring engineers for their in-house teams, Microsoft, Amazon, and Google employ thousands of cloud engineers to work on their infrastructure as a service (IaaS), platform as a service (PaaS), and serverless computing environments.

Unsurprisingly, this growth in the demand and applications for cloud computing has had a direct impact on company hiring needs and the career outlook of cloud engineering.

In 2020, there were 775,022 cloud computing jobs posted, up 94% from 400,500 jobs posted just three years earlier. In comparison, all tech job postings grew around 20% during that period. That means the demand for cloud computing and engineering talent is growing nearly five times faster than the rest of the tech industry. 

It’s no understatement to say this explosion of cloud engineering demand has created one of the biggest talent needs in the tech industry.

This gap between supply and demand has had a direct impact on the companies unable to meet their hiring needs, keeping many businesses out of the cloud. As the cloud computing industry continues to grow, competition for great cloud talent is fierce and will be for the foreseeable future.

Types of Cloud Engineer Positions

The titles cloud engineers hold vary drastically, depending on their experience, education, and the company they work at. The title of a graduate from a coding bootcamp might look different than a candidate with a four-year degree. And the role of a cloud engineer in a five-person startup will be different than at a 5,000 person company.

At the beginning of their career, a cloud engineer will start out with an entry-level role, like Cloud Engineer I or Junior AWS Engineer. New cloud engineers will typically start their careers by working on internal or external project solutions along with systems and integration testing. They can expect to work in one of these roles for one to three years.

From there, they’ll have the opportunity to move into more senior-level roles with hands-on engineering experience, such as: 

• Senior Cloud Engineer
• Cloud Engineer II
• Cloud Engineering Manager
• Cloud Security Engineer
• Senior AWS Engineer
• Systems Engineer
• Cloud Developer
• Cloud Architect
• Network Engineer

While they spend several years honing their skills, their responsibilities expand to include taking ownership of projects, working independently in a team environment, and mentoring project team members. Senior cloud engineers might also choose to specialize in a particular technology or discipline, such as cloud security or DevOps.

With some experience under their belt, a cloud engineer often faces a crossroads in their career having to choose between two paths. 

The first path is to pivot into people and team management functions. Hiring, mentoring, resource planning and allocation, strategy, and operations become a larger component of the responsibilities of cloud engineers pursuing this career path. At the higher levels of an organization, these titles include:

• Director of Cloud Networking
• Cloud Operations Manager
• Director of Solutions Architecture
• Chief Information Officer (CIO)
• Chief Technology Officer (CTO)

The second possible career path is to continue as an individual contributor. Many cloud engineers opt to continue their careers as individual contributors, enjoying equally fulfilling careers and developing deeper technical expertise in various languages and frameworks.

The motivation behind this decision is that experienced cloud engineers aren’t necessarily interested in or qualified to be managing a team. And an engineer in an individual contributor role has the opportunity to focus on growing their technical skills and learning the newest emerging technologies.

Data is scarce on how this career decision will impact long-term earning potential. Career outlook for individual contributors and managers will also depend on a number of other factors, including industry, company size, and experience.

Salary Comparisons & Job Outlook

On average, cloud engineers tend to receive a salary significantly higher than the national average in their country.

For example, in the U.S. the average salary in 2020 was $53,400. In contrast, the average base salary for cloud engineers in the U.S. is $114,323 to $130,977. That’s 114 to 145 percent more than the national average.

Junior cloud engineers can expect to occupy a lower salary band at the beginning of their careers. In contrast, senior positions provide a higher average compensation, though data for this specific salary band is hard to find. Industry and company size also affect the salary band dramatically. Historically, though, geography has had a significant impact on the compensation of technical talent — and that includes cloud engineers. 

Cloud Engineering Skills

Technical Skills

Cloud engineers use a range of technologies to build cloud-based platforms, infrastructure, and applications. 

A core requirement of cloud engineering is expertise in the technologies offered by cloud-hosting providers. These include, to name a few:

• AWS
• Azure
• GCP
• IBM Cloud
• Oracle Cloud

Recruiters and hiring managers who are hiring cloud engineers should look for in-demand competencies with the specific services and products offered by these platforms. An AWS cloud engineer, for example, might be familiar with Amazon cloud products such as Glue, Lake Formation, Redshift, Athena, MSK, and Kinesis.

Many cloud engineering roles require knowledge of data-oriented and object-oriented languages such as Java, Ruby, Python, or Clojure. Some roles also require familiarity with a general programming language, such as C, C+, C#, or Go. 

Depending on the role, software engineers might also work with a number of other tools:

• Databases (SQL, MySQL, NoSQL)
• Message brokers (RabbitMQ, ActiveMQ, Kafka)
• Big data frameworks (Apache Hadoop, Apache Spark)
• Familiarity with web services and APIs:
  • XML (Extensible Markup Language)
  • SOAP (Simple Object Access Protocol)
  • WSDL (Web Services Description Language)
  • UDDI (Universal Description, Discovery and Integration)

Some cloud engineers will specialize in DevOps, a set of practices that combines software development and IT operations. Core DevOps skills include:

• Container management software (Docker, Kubernetes)
• CI/CD tools (Gitlab, Jenkins, Nexus)
• Linux Fundamentals And Scripting
• Infrastructure as Code (IaC)

Security and Compliance

Some cloud engineers specialize in keep computing resources and data secure, operational, and compliant. Cloud cybersecurity engineers typically need an understanding of cloud cybersecurity to create cloud backup strategies in preparation for outages, natural disasters, and cyberattacks. They also need the skills to identify regulations and guidelines for compliance.

Soft Skills

Technical competency alone isn’t enough to succeed in a cloud engineering role. Mathematical, analytical, and problem-solving skills are a must in any technical role. And soft skills are even more critical in a digital-first or digital-only environment.

Employers may put even more stock into engineers with strong soft skills, such as:

• Time management
• Communication
• Project management
• Problem solving

Communication skills, in particular, are critical to cloud engineering. Cloud engineers often work in client-facing or consulting roles that require them to communicate complex information to stakeholders in other departments or companies. The ability to turn technical subject matter into easy-to-understand solutions is highly valuable to cloud engineers — and the teams that employ them.

Certifications

All of the major cloud providers offer certification courses to cloud engineers, including AWS, Azure, and GCP. In addition to providing training in the platforms, certifications also serve as a credential for cloud engineers. For roles requiring experience with cloud providers, having a certification in the appropriate platform is a mandatory or nice-to-have qualification. 

Experience & Education

After competency, the most important qualification for cloud engineers is experience. On-the-job experience and training is a critical requirement for many employers.

Then, there’s the question of education. The education requirements for cloud engineering positions vary widely. Many employers still require cloud engineering candidates to have four-year degrees. Some might even expect graduate-level degrees.

But competition for skilled cloud engineers is fierce, and it’s common for job openings requiring degrees to go unfilled. There are simply not enough engineers with degrees to fill thousands of open roles out there. Companies looking to hire cloud engineers will have access to a much larger pool of talent and achieve their cloud initiatives if they recognize other forms of education and experience. 

Resources for Hiring Cloud Engineers

Developer Hiring Solutions

How to Assess Cloud Engineering Candidates

The post What Does a Cloud Engineer Do? Job Overview & Skill Expectations appeared first on HackerRank Blog.