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Find out all about Google Cloud's latest learning path, and learn how to use the Gemini language model in the Google Cloud.View the full article

Cloud computing is the delivery of computing services, such as servers, storage, databases, networking, software, analytics, and intelligence, over the internet. It enables faster innovation, efficient resource usage, and economies of scale. It also reduces the need for hardware maintenance, software updates, and security patches. The demand for cloud skills is high and growing as more and more organizations adopt cloud-based solutions for their business needs. According to the latest forecast from Gartner, worldwide end-user spending on public cloud services is expected to reach $679 billion in 2024, a 20% growth from 2023. The report also predicts that by 2027, more than 70% of enterprises will use industry cloud platforms to accelerate their business initiatives. If you want to become a cloud expert and advance your career as a DevOps engineer, you need to learn the fundamentals, choose a platform, get hands-on experience, gain certifications, and continue on-the-job learning. In this article, we will guide you through these steps and help you achieve your cloud computing goals. Why cloud computing?Before diving into the details of how to learn cloud computing, let's first understand why cloud computing is so important for DevOps engineers. Cloud computing offers several advantages over traditional on-premises computing. Below are some of them: Cost-efficiency: Cloud computing eliminates the upfront cost of buying and maintaining hardware and software, as well as the operational cost of power, cooling, and security. You only pay for what you use, and you can scale up or down as needed.Scalability: It allows you to access unlimited resources on demand without worrying about capacity planning or provisioning. You can easily handle spikes in traffic, data, or workload and scale back when not needed.Performance: Cloud computing provides high-performance computing resources that are optimized for different types of workloads, such as compute-intensive, memory-intensive, or network-intensive. You can also leverage the global network of data centers and edge locations to reduce latency and improve user experience.Reliability: It ensures the availability and durability of your data and applications by replicating them across multiple servers and regions. You can also use backup, recovery, and failover features to prevent data loss and downtime.Security: Cloud computing offers built-in security measures, such as encryption, firewalls, identity and access management, and compliance standards. You can also use additional tools and services to enhance your security posture and protect your data and applications.Innovation: It enables you to experiment and innovate faster by providing access to the latest technologies and services. For instance, artificial intelligence, machine learning, big data, IoT, and serverless computing. You can also integrate and orchestrate different services to create new solutions and value propositions.As a DevOps engineer, you can leverage these benefits to deliver better products and services faster and more efficiently. Learn about Cloud ComputingTo learn cloud computing, you need to understand the basic concepts and principles that underpin it. You also need to familiarize yourself with the different types of cloud computing and the major cloud platforms that offer them. Types of Cloud ComputingThere are three main types of cloud computing, based on the level of abstraction and control they provide: Infrastructure as a Service (IaaS): This is the most basic type of cloud computing, where you rent servers, storage, and networking resources from a cloud provider. It gives you full control over the resources’ configuration and management. Examples of IaaS providers include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).Platform as a Service (PaaS): This is a type of cloud computing where you use a cloud provider's platform to develop, deploy, and run your applications without worrying about the underlying infrastructure. The cloud provider manages the servers, storage, and networking resources, as well as the operating system, middleware, and runtime environment. You only focus on the code and the logic of your applications. Examples of PaaS providers are AWS Elastic Beanstalk, Azure App Service, and Google App Engine.Software as a Service (SaaS): This is a type of cloud computing where you use a cloud provider's software applications over the internet without installing or maintaining them on your own devices. The cloud provider manages the infrastructure, platform, and application, and you only access them through a web browser or a mobile app. Examples of SaaS providers are Gmail, Salesforce, and Zoom.Check our article on “What is Cloud Computing” to learn more about cloud computing services, cloud computing deployment model, its advantages and limitations. Cloud PlatformsThere are many cloud platforms that offer different types of cloud computing services, but the top three that dominate the market are AWS, Azure, and GCP. These platforms have their own strengths and weaknesses, and you need to compare and contrast them to decide which one suits your needs and preferences. Below are some of the factors that you need to consider when choosing a cloud platform: Features and Services: Each cloud platform offers a variety of features and services, covering different domains and use cases, such as compute, storage, database, networking, security, analytics, AI, IoT, etc. You need to evaluate the quality, quantity, and diversity of the features and services that each platform offers and how they match your requirements and expectations.Pricing and Billing: The pricing and billing model are based on different parameters, such as resource type, usage, duration, region, etc. You need to understand the pricing and billing structure of each platform and how it affects your budget and spending. You also need to compare the cost-effectiveness and value proposition of each platform and how they align with your goals and outcomes.Documentation and Support: Go through their documentation to assess the support resources that each platform offers and how they help you learn and troubleshoot. You also need to consider the availability and responsiveness of the customer service and technical support that each platform provides.These are some of the factors you need to consider when choosing a cloud platform, but there may be others depending on your specific needs and preferences. You also need to recognize any gaps that you may have in your existing knowledge and skills before starting an in-depth learning journey. For example, you may need to brush up on your programming, scripting, or networking fundamentals or learn some new tools or frameworks that are relevant for cloud computing. Choose a Platform to Focus OnOnce you have a general understanding of cloud computing and its types, concepts, and platforms, you need to choose a platform to focus on for your learning. While it is possible to learn multiple cloud platforms, it is advisable to start with one platform initially and gain depth over breadth. This will help you master the core concepts and services of that platform and build confidence and competence in using them. The question then is, which platform should you choose? The answer depends on several factors, such as your personal interest, career goals, project requirements, employer preferences, etc. However, if you are looking for a general recommendation, we suggest picking AWS as your first cloud platform to learn. AWS is the oldest and largest cloud platform in the market, with a global presence and a dominant market share. According to a report by Synergy Research Group, AWS had a 31% share of the cloud infrastructure services market in Q4 2023, followed by Azure with 24% and GCP with 11%. AWS also had the highest annual revenue growth rate of 28% among the top three cloud platforms. AWS offers a comprehensive and diverse range of cloud services, covering almost every domain and use case imaginable, such as compute, storage, database, networking, security, analytics, AI, IoT, etc. It also has a rich and mature ecosystem of partners, customers, and developers who create and share valuable resources and solutions. It has a well-established and reputable certification program, which validates your cloud skills and knowledge and enhances your credibility and employability. Of course, this does not mean that AWS is the best or the only cloud platform to learn. Azure and GCP are also excellent cloud platforms with their own strengths and advantages, such as integration with Microsoft and Google products, respectively. You may also want to learn about other cloud platforms, such as IBM Cloud, Oracle Cloud, or Alibaba Cloud, depending on your specific needs and preferences. The important thing is to choose a platform that aligns with your learning objectives and outcomes and stick with it until you master it. How to Learn Cloud ComputingAfter choosing a cloud platform to focus on, you need to learn how to use it effectively and efficiently. There are many ways to learn cloud computing, but we recommend the following three steps: get hands-on experience with projects, gain cloud certifications, and continue on-the-job learning. Get Hands-On Experience with ProjectsThe best way to learn cloud computing is by doing it. You need to get hands-on experience with the cloud platform and its services by creating and deploying real-world projects. This will help you apply the concepts and principles that you learned and develop the skills and confidence that you need. You can start by following some tutorials or courses that guide you through the basics of the cloud platform and its services and show you how to create and deploy simple applications. However, you should not stop there. You should also create your own projects based on your own ideas and interests, and challenge yourself to use different services and features. Below are examples of projects that you can create and deploy on the cloud platform: Automate the infrastructure deployment of your applications using tools like Terraform, CloudFormation, or ARM templates. This will help you learn how to use infrastructure as code (IaC), which is a key skill for DevOps engineers.Build a full-stack web application using services like EC2, S3, RDS, DynamoDB, Lambda, API Gateway, etc. This will help you learn how to use different types of compute, storage, and database services and how to integrate and orchestrate them.Create a containerized application using services like Docker and orchestrate it using Kubernetes, EKS, AKS, or GKE. This will help you learn how to use containers and orchestration tools essential for microservices architectures and DevOps practices.Develop a serverless application using services like Lambda, Azure Functions, or Cloud Functions. This will help you learn how to use serverless computing, a popular and powerful paradigm for cloud development.Check out the following courses and articles to help you with the projects above: What is Infrastructure-as-Code (IaC)? To understand more about IaCCI/CD Learning Path, to learn more about integrating and orchestrationWhat is Containerization? To learn more about containerization You can use our Cloud playground, which gives you access to AWS, Azure, and Google Cloud in a controlled environment. They allow you to learn without fear of failure. Gain Cloud CertificationsCertifications validate technical proficiency and signal commitment. Cloud certifications can help you fill gaps in your learning and prepare you for real-world scenarios and challenges. There are many cloud certifications available, but we recommend you check our Cloud Learning Path for a comprehensive guide on the cloud certifications we offer. Whether you are interested in AWS, Azure, or GCP, we have a curated list of courses and resources to help you get started and advance your career in cloud computing. Continue Learning on the JobWith technology evolving relentlessly, cloud computing is never fully learned. Continuous learning is an absolute must. Participate in available company training programs or conferences to stay up to date on the latest tools and best practices. Seek opportunities to assist coworkers and learn new services through collaborations. Consider specializing further in emerging services by taking on side projects leveraging AI/ML, 5G, edge computing, or other disruptive innovations. ConclusionCloud computing is an invaluable skill for DevOps engineers, who need to develop, deploy, and operate applications in a fast and reliable manner. With focus and perseverance, you will establish the coveted experience and skillset to become a highly valued cloud expert. If you want to take your career to the next level, sign up on KodeKloud for free now and learn how to use cloud computing on the go. You can also check out our Cloud Learning Path to get started with cloud computing. Cloud Learning Path | KodekloudChart your Cloud learning journey with our expert-designed learning path and study roadmap.CloudView the full article

Get a cloud education with this training bundle for just $32 when using code ENJOY20 at checkout. Offer ends April 16th.View the full article

Learning the different AWS services and how they work can be a daunting task, especially if you’re new to the cloud. Last year, we wrote about how you can use AWS Solution Focused Immersion Days (SFID) to accelerate your team’s understanding of different aspects of AWS. There are many resources available to help you build your knowledge and skills, such as AWS Whitepapers and 600+ free digital learning courses and resources on AWS Skill Builder, our online learning center. Today, we want to introduce you to the resources that power SFIDs – AWS Workshops. AWS Workshops are free, self-guided tutorial experiences that give you hands-on experience with AWS services. Through technical step-by-step modules, created by teams at Amazon Web Services (AWS), any level of learner can build an understanding of AWS Cloud concepts along with practical skills and techniques. If you’re new to cloud, have no fear: there is plenty here for you too! What can I learn via an AWS Workshop? We have 1000+ AWS Workshops available today, with more being added and updated each week, across a range of topics – including generative artificial intelligence (AI), machine learning (ML), big data and analytics, serverless, databases, security, and more. If you’re looking for service-specific, domain-specific, or use-case focused workshops, we’ve got you covered. Check out “How to use IAM Policies” (service-specific) or “Use Generative AI to build a DevSecOps Chatbot” (use-case specific). Each AWS Workshop is based on common use-cases and customer and partner feedback. You can find a workshop by using the search toolbar at the top of the AWS Workshops homepage or by directly searching service names or use cases. What is the experience like? Your workshop is structured with step-by-step instructions, from how to set up your AWS environment in preparation for the workshop, to hands-on modules that cover sub-domains and use-cases for the workshop at hand. The learning is all self-paced, allowing you to gain hands-on experience in the AWS Management Console, CLI, and SDK, with the ability to stop and start as often as needed. The artifacts from these workshops can be used in your own AWS account and can even help set the foundation for future AWS projects and initiatives. Workshops vary by complexity and estimated time to completion, but there is no time limit. Short workshops take as little as one hour to complete, and deep dive workshops that walk through multiple services and concepts may take as long as six hours to complete. How to get started with AWS Workshops Step 1: Create an AWS account If you’re interested in learning using AWS Workshops, the first step is to create your own AWS account. When you open a new AWS account, take advantage of a few offers, including free trials for certain services, which are detailed in the AWS Free Tier. Please note that some workshops may incur small charges in your AWS account, depending on the resources that are provisioned, but many workshops deploy resources that are partially, if not entirely, covered under the AWS Free Tier. Please keep in mind that if you do not terminate resources from the workshops after completing them, you may incur unexpected charges in your AWS Account. We’ll cover how to clean up the resources provisioned in your AWS account by in Step 5. Step 2: Select your workshop Once you have an AWS account, go to AWS Workshops to start by searching for the topic you’d like to learn more about. For example, if you search “generative AI”, you should see AWS Workshops that leverage AWS generative AI services. See image 1 below. Workshops are categorized by technology domains and AWS services. Be sure to check that your topic is in either the “Categories” or “Tags” label. Sometimes, the search engine will identify workshops that minimally utilize the topic that you searched, so these workshops may not be a good fit based on what you’re interested in learning. As you’re reviewing the search results, keep in mind two things: 1/ the level of the workshop and 2/ the estimated time for completion. Each workshop is assigned a complexity level and there are four levels: 100, 200, 300, and 400. Level 100 are introductory-level focusing on a general overview of AWS services and features. Level 200 are intermediate-level and assume that the learner has an introductory level of knowledge on the topic and is ready to get deeper into the details of AWS services. Level 300 are advanced and dive deep into the selected topic, assuming the learner has familiarity with the topic but may not have any experience implementing a solution. Level 400 are expert-level and focus on advanced architectures and implementations. This level is typically best suited for a learner with experience implementing similar solutions. Each workshop has an estimated time for completion, assuming the learner will understand all instructions and can complete tasks accordingly. However, based on your level of AWS and technology-specific experience, this time could vary, and you are free to take as much time as you need to complete the workshop. Once you’ve chosen a workshop, click the “Get Started” button. Step 3: Set up the AWS Environment AWS Workshops can be run as part of an AWS event led by AWS Solutions Architects (such as an Immersion Day) or run in your own AWS account. In today’s blog, we’re focused on how to run the AWS Workshops in your own AWS account. There are tabs on the left side of the AWS Workshop interface that allow you to navigate through different parts of the workshop. Consistent tabs include: “Setup”, “How to Start”, and “Start Building” (or something similar). See image 2 below. To run the workshop environment in your own AWS account, you’ll select the “Self-Paced”, “Use Your Own AWS Account”, and “Customer-owned Account Setup” (or a similarly named tab). This step typically entails deploying specific per-requisite AWS resources that are necessary to complete the rest of the workshop. Don’t worry about having to keep track of resources that you create. At the end of each AWS Workshop, it will walk you through cleaning up all resources that you created. Step 4: Complete modules Each AWS Workshop may have one or more modules, each addressing a specific topic in the workshop. Some modules can be completed independent of one another; others must be completed in sequence. We recommend always completing all modules, in order, for a given AWS Workshop. Completing each module in sequence ensures that not only are all necessary resources created for subsequent modules, but also that you understand the broader concepts behind the workshop. Please note that some workshop modules may ask you to download and upload files. These files are created and secured by AWS workshop teams. Step 5: Cleanup At the end of each AWS Workshop, we want to ensure all provisioned resources are terminated so we can return your AWS account to its original configuration. In every AWS Workshop, there is a tab labelled “Cleanup” or “Clean Up Resources”. Be sure to follow the outlined steps to terminate all resources created throughout the AWS Workshop. This ensures no unexpected AWS charges or security risks present themselves in your AWS account. If you are looking for a more guided approach to AWS Workshops, please read about AWS Solutions Focused Immersion Days. We hope you get a chance to leverage AWS Workshops to learn more about how to use AWS and wish you the best of luck in your AWS journey. Additional resources In addition to AWS Workshops, you can get hands-on learning with AWS Skill Builder subscriptions for access to 195+ AWS Builder Labs, enhanced exam prep resources, AWS Cloud Quest, AWS Industry Quest, AWS Jam Journeys, and more. There’s something for every cloud learner, from brand new builder to experienced professional. Use the 7-day free trial of AWS Skill Builder Individual subscription* to access it all free. *terms and conditions apply View the full article

Continuous learning is a necessity for developers in today’s fast-paced development landscape. Docker recognizes the importance of keeping developers at the forefront of innovation, and to do so, we aim to empower the developer community with comprehensive learning resources. Docker has taken a multifaceted approach to developer education by forging partnerships with renowned platforms like Udemy and LinkedIn Learning, investing in our own documentation and guides, and highlighting the incredible learning content created by the developer community, including Docker Captains. Commitment to developer learning At Docker, our goal is to simplify the lives of developers, which begins with empowering devs with understanding how to maximize the power of Docker tools throughout their projects. We also recognize that developers have different learning styles, so we are taking a diversified approach to delivering this material across an array of platforms and formats, which means developers can learn in the format that best suits them. Strategic partnerships for developer learning Recognizing the diverse learning needs of developers, Docker has partnered with leading online learning platforms — Udemy and LinkedIn Learning. These partnerships offer developers access to a wide range of courses tailored to different expertise levels, from beginners looking to get started with Docker to advanced users aiming to deepen their knowledge. For teams already utilizing these platforms for other learning needs, this collaboration places Docker learning in a familiar platform next to other coursework. Udemy: Docker’s collaboration with Udemy highlights an array of Endorsed Docker courses, designed by industry experts. Whether getting a handle on containerization or mastering Docker with Kubernetes, Udemy’s platform offers the flexibility and depth developers need to upskill at their own pace. Today, demand remains high for Docker content across the Udemy platform, with more than 350 courses offered and nearly three million enrollments to date. LinkedIn Learning: Through LinkedIn Learning, developers can dive into curated Docker courses to earn a Docker Foundations Professional Certificate once they complete the program. These resources are not just about technical skills; they also cover best practices and practical applications, ensuring learners are job-ready. Leveraging Docker’s documentation and guides Although third-party platforms provide comprehensive learning paths, Docker’s own documentation and guides are indispensable tools for developers. Our documentation is continuously updated to serve as both a learning resource and a reference. From installation and configuration to advanced container orchestration and networking, Docker’s guides are designed to help you find your solution with step-by-step walk-throughs. If it’s been a while since you’ve checked out Docker Docs, you can visit docs.docker.com to find manuals, a getting started guide, along with many new use-case guides to help you with advanced applications including generative AI and security. Learners interested in live sessions can register for upcoming live webinars and training on the Docker Training site. There, you will find sessions where you can interact with the Docker support team and discuss best practices for using Docker Scout and Docker Admin. The role of community in learning Docker’s community is a vibrant ecosystem of learners, contributors, and innovators. We are thrilled to see the community creating content, hosting workshops, providing mentorship, and enriching the vast array of Docker learning resources. In particular, Docker Captains stand out for their expertise and dedication to sharing knowledge. From James Spurin’s Dive Into Docker course, to Nana Janashia’s Docker Crash Course, to Vladimir Mikhalev’s blog with guided IT solutions using Docker (just to name a few), it’s clear there’s much to learn from within the community. We encourage developers to join the community and participate in conversations to seek advice, share knowledge, and collaborate on projects. You can also check out the Docker Community forums and join the Slack community to connect with other members of the community. Conclusion Docker’s holistic approach to developer learning underscores our commitment to empowering developers with knowledge and skills. By combining our comprehensive documentation and guides with top learning platform partnerships and an active community, we offer developers a robust framework for learning and growth. We encourage you to use all of these resources together to build a solid foundation of knowledge that is enhanced with new perspectives and additional insights as new learning offerings continue to be added. Whether you’re a novice eager to explore the world of containers or a seasoned pro looking to refine your expertise, Docker’s learning ecosystem is designed to support your journey every step of the way. Join us in this continuous learning journey, and come learn with Docker. Learn more Subscribe to the Docker Newsletter. Get the latest release of Docker Desktop. Vote on what’s next! Check out our public roadmap. Have questions? The Docker community is here to help. New to Docker? Get started. View the full article

Want to switch to a tech career? Make it happen with these free computer science courses.View the full article

This article introduces six top-notch, free data science resources ideal for aspiring data analysts, data scientists, or anyone aiming to enhance their analytical skills.View the full article

Looking to learn SQL and databases to level up your data science skills? Learn SQL, database internals, and much more with these free university courses.View the full article

Research shows that developers complete tasks 55% faster at higher quality when using GitHub Copilot, helping businesses accelerate the pace of software development and deliver more value to their customers. We understand that adopting new technologies in your business involves thorough evaluation and gaining cross functional alignment. To jump start your organization’s entry into the AI era, we’ve partnered with engineering leaders at some of the most influential companies in the world to create a new expert-guided GitHub Learning Pathway. This prescriptive content will help organizational leaders understand: What can your business achieve using GitHub Copilot? How does GitHub Copilot handle data? What are the best practices for creating an AI governance policy? How can my team successfully roll out GitHub Copilot to our developers? Along the way, you’ll also get tips and insights from engineering leaders at ASOS, Lyft, Cisco, CARIAD (a Volkswagen Group company), and more who have used GitHub Copilot to increase operational efficiency, deliver innovative products faster, and improve developer happiness! Start your GitHub Copilot Learning Pathway Select your GitHub Learning Pathway NEW! AI-powered development with GitHub Copilot From measuring the potential impact of GitHub Copilot on your business to understanding the essential elements of a GitHub Copilot rollout, we’ll walk you through everything you need to find success with integrating AI into your businesses’ software development lifecycle. CI/CD with GitHub Actions From building your first CI/CD workflow with GitHub Actions to enterprise-scale automation, you’ll learn how teams at leading organizations unlock productivity, reduce toil, and boost developer happiness. Application Security with GitHub Advanced Security Protect your codebase without blocking developer productivity with GitHub Advanced Security. You’ll learn how to get started in just a few clicks and move on to customizing GitHub Advanced Security to meet your organization’s unique needs. Administration and Governance with GitHub Enterprise Configure GitHub Enterprise Cloud to prevent downstream maintenance burdens while promoting innersource, collaboration, and efficient organizational structures, no matter the size and scale of your organization. Learning Pathways are organized into three modules: Essentials modules introduce key concepts and build a solid foundation of understanding. Intermediate modules expand beyond the basics and detail best practices for success. Advanced modules offer a starting point for building deep expertise in your use of GitHub. We are hard at work developing the next GitHub Copilot Learning Pathway module, which will include a deep dive into the nitty-gritty of working alongside your new AI pair programmer. We’ll cover best practices for prompt engineering and using GitHub Copilot to write tests and refactor code, among other topics. Are you ready to take your GitHub skills to the next level? Get started with GitHub Learning Pathways today.

The future world is full of LLM, and you don’t want to miss this most sought skill.View the full article

Learning as a tech professional never ends, so keep up to date with these learning platforms.View the full article

The post How to Create Own Online Learning Platform with Moodle in Linux first appeared on Tecmint: Linux Howtos, Tutorials & Guides .Moodle is a free, feature-rich, open-source learning management system (LMS), which is used by many online schools and universities as well as private educators. Moodle The post How to Create Own Online Learning Platform with Moodle in Linux first appeared on Tecmint: Linux Howtos, Tutorials & Guides.View the full article

We’re back with the February course launches and certification updates from AWS Training and Certification to equip you and your teams with the skills to work with AWS services and solutions. This month we launched 22 new digital training products on AWS Skill Builder, including new AWS Digital Classroom courses for annual subscribers, five new solution assignments to build generative AI skills via AWS Cloud Quest, and new prep courses for the AWS Certified Data Engineer – Associate exam. Don’t forget to try the 7-day free trial of AWS Skill Builder Individual subscription* for access to our most immersive, hands-on trainings, including 195+ AWS Builder Labs, enhanced exam prep resources, AWS Cloud Quest, AWS Industry Quest, AWS Jam Journeys, and more. There’s something for every cloud learner, from brand new builder to experienced professional. *terms and conditions apply New Skill Builder subscription features The following new AWS Skill Builder features require an Individual or Team subscription. Individuals can try for free with a 7-day free trial. AWS Digital Classroom Get access to a catalog of AWS Classroom Training courses that have the flexibility of digital training with the depth of classroom training. Available with an annual subscription for individuals or teams, learn more about AWS Digital Classroom and subscribe today — and for a limited time, receive $150 off your annual Individual plan. AWS Cloud Quest AWS Cloud Quest has added five solution assignments to build practical generative AI skills within Machine Learning, Serverless Developer, Solutions Architect, and Security roles. Learn to generate images from text descriptions, create chatbots powered by large language models, use generative AI to build cloud infrastructure, and monitor compute resources using AI-generated code. These hands-on assignments will teach you how to leverage services like Amazon CodeWhisperer, Amazon Lex V2, and Amazon SageMaker for applied generative AI and automation. AWS Certification exam prep and updates Three AWS Certifications are retiring AWS Certification will retire three specialty certifications and their corresponding AWS Skill Builder Exam Prep trainings in April 2024: AWS Certified Data Analytics – Specialty on April 9, 2024; and AWS Certified Database – Specialty and AWS Certified: SAP on AWS – Specialty on April 30, 2024. If you plan to earn these credentials, be sure to take your exam prior to their retirement dates. Exam prep resources Exam Prep Standard Course: AWS Certified Data Engineer – Associate (DEA-C01 – English) (6 hours) is a free digital course designed to prepare you for the AWS Certified Data Engineer – Associate (DEA-C01) exam. During this course you’ll follow a step-by-step plan to prepare, gauging your understanding of topics and concepts from each task statement grouped by exam domains. Become an AWS Skill Builder subscriber and access an enhanced subscription-only 13-hour Exam Prep Enhanced Course: AWS Certified Data Engineer – Associate (DEA-C01 – English) that includes hands-on exercises and exam-style questions to reinforce your knowledge and identify learning gaps. You’ll explore learning strategies to identify incorrect responses to help you determine your readiness to take the exam with the AWS Certification Official Pretest. Exam Prep Official Pretest: AWS Certified Data Engineer – Associate (DEA-C01) (2 hours) helps you prepare for the AWS Certified Data Engineer – Associate (DEA-C01) exam. Gain confidence going into exam day with an official, full-length pretest created by the experts at AWS. Take an AWS Certification Official Pretest to focus your preparation where you need it most, and assess your exam readiness. Exam Prep Official Pretest: AWS Certified Cloud Practitioner (CLF-C02) is now available in French, Italian, German, Spanish-Spain, Traditional Chinese and Indonesian (English, Japanese, Korean, Portuguese, Simplified Chinese, and Spanish LatAm already available). Free digital courses on AWS Skill Builder The following digital courses are free within AWS Skill Builder, along with 600+ other digital courses, learning plans, and resources. Fundamental courses AWS Skill Builder Learner Guide (15 min.) teaches new users how to navigate through AWS Skill Builder and what content types are available to learners. AWS for SAP Fundamentals (45 min.) teaches you the essentials of SAP architecture and provides an understanding of various AWS adoption scenarios for SAP, licensing options, and the AWS support frameworks specific to SAP workloads on AWS. You’ll acquire a foundational knowledge of the basics involved in operating SAP in the AWS Cloud. AWS Mainframe Modernization Refactor with AWS Blu Age Getting Started (60 min.) teaches you the functionality, technical architecture, key use cases and cost structure of AWS Mainframe Modernization Refactor with AWS Blu Age. AWS Mainframe Modernization Replatform with Micro Focus Getting Started (60 min.) teaches you the functionality, technical architecture, key use cases and cost structure of AWS Replatform with Micro Focus. Intermediate courses Containerize and Run .NET Applications on Amazon EKS Windows Pods (2 hours) teaches you Kubernetes, an open-source system for automating deployment, scaling, and managing containerized applications. It also covers Amazon Elastic Kubernetes Service (Amazon EKS), a managed service to run a Kubernetes workload on AWS without the need to install, operate, and maintain your own Kubernetes cluster. Amazon QuickSight Advanced Business Intelligence Authoring (Part 1) (90 min.) teaches you how to author business intelligence experiences using Amazon QuickSight. In this first course of a two-part series, you’ll dive into advanced authoring capabilities in QuickSight, gain expertise in data connectivity, data preparation, and customized highly formatted dashboard building. Amazon QuickSight Advanced Business Intelligence Authoring (Part 2) (90 min.) teaches you how to author business intelligence experiences using Amazon QuickSight. In this second course of a two-part series, you’ll gain practical knowledge on building interactivity, including filters, actions, navigation, and sheets, QuickSight security, QuickSight Q, forecasting, paginated reporting, and data export. AWS Mainframe Modernization – Using MicroFocus Managed Runtime Environment (60 min.) teaches you to build an AWS Replatform with Micro Focus environment using an AWS CloudFormation template to deploy and test an application. AWS Mainframe Modernization – Using Refactor Tools (60 min.) teaches you to setup AWS Blu Insights and use code import and transformation features to refactor Mainframe application code. Amazon Timestream – Data Modeling Techniques (60 min.) teaches you about the significance of efficiently modeling data for your time series workloads using Amazon Timestream. You’ll be introduced to various Timestream features and how to use them for different scenarios. At the end of this course you’ll be able to implement high-performance data models for Amazon Timestream. AWS Training for Partners AWS Partner: SAP on AWS (Technical) (3.5 hours) teaches you key architecture patterns for SAP on AWS, with emphasis on designing, migrating, implementing, and managing SAP solutions. You’ll also gain an understanding of SAP HANA on AWS, and high availability and disaster recovery scenarios. Successfully complete the final assessment and you’ll earn a Credly Accreditation Badge. View the full article

Amazon Q is a generative-AI powered assistant that helps customers answer questions, provide summaries, generate content, and complete tasks based on data in their company repository. It also exists as a learning tool for AWS users who want to ask questions about services and best practices in the cloud. Amazon Q is integrated into AWS tools to assist readers and builders in learning services quickly, troubleshooting in the AWS Management Console, and much more, essentially working as an “AWS assistant” as users build. To use Amazon Q, you just need to sign into your AWS account and enter a question into the text bar in the Amazon Q panel. Amazon Q then generates a response to the question including a section with sources, that link to its references. After you receive a response, you can optionally leave feedback by using the thumbs-up and thumbs-down icons to strengthen the capabilities of the tool. In this blog, we’ll share how you, whether you’re technical or not, can use Amazon Q to accelerate and streamline your journey of learning how to build with AWS services. Use Amazon Q for AWS Documentation assistant Often, the first step in learning a new service is through that service’s front page and its documentation. These resources provide you with a foundation before you progress into hands on learning through building. As your cloud journey continues, documentation becomes an important tool in troubleshooting and customizing your workload. It’s no surprise though that many readers find AWS whitepapers and documentation long and complicated. As you read through a page you may run into an unknown technical term or an unfamiliar service feature. Rather than gear switching between multiple documents, you can now use the Amazon Q assistant to ask questions and get answers in real time! Just look for the Q icon on the right-hand side of any public AWS whitepaper, service front page, or documentation guide. You can see in the below example, while reading about best practices for snapshotting database clusters in Amazon Aurora, we want to understand if it is possible to automate the process. By asking Q, “Can I run automated snapshots on Amazon Aurora?” we receive concise details as well as link to the reference pages to learn more. I can ask quick clarifying questions and also receive targeted resources for further reading. Use the Amazon Q assistant to ask questions and get answers in real time! Just look for the Q icon on the right-hand side of any public AWS whitepaper, service front page, or documentation guide. As mentioned previously, Amazon Q is also available on each AWS service page. Below you can see we are on the Amazon Simple Storage Service (S3) service page and open up Amazon Q icon, which can also be found bottom right of the page. You are able to choose one of the prompts to get started or start asking Amazon Q service-specific questions in order to learn more about S3. By leveraging the Amazon Q chatbot to ask clarifying questions in real time, you no longer have to leave the page to dive deeper, providing a mechanism to help you remain focused. AWS Console assistant Your next step after reading documentation is likely to start building in the AWS Console. We often see that learners are kinesthetic and like to build as a way to better digest content, whether it’s through workshops, independent experimentation, or a guided in-person session. In these situations, there can often be more gear-shifting and/or getting lost in reading when a question arises mid-build. Now, you can find Amazon Q AWS expert in the console and ask your questions through the build process. Currently, Amazon Q AWS expert is in “preview” release and the use of expert assistant for AWS is available for no additional charges during the preview. This allows you to chat with the AWS expert Amazon Q assistant in the AWS Management Console, documentation, and AWS website. You can check out the additional details. You can find Amazon Q AWS expert in the AWS Management Console and ask your questions through the build process. After logging into the AWS Console, regardless of the service, you’ll find the Amazon Q icon on the right-hand side. The chatbot here functions in the same way as described above. Just type out your questions and Amazon Q will generate an answer with sources cited. In the console, learners have the opportunity to ask Amazon Q questions about AWS services, best practices, and even software development with the AWS SDKs and AWS CLI. Amazon Q in the console can generate short scripts or code snippets to help you get started using the AWS SDKs and AWS CLI. The following are example questions that demonstrate how Amazon Q can help you build on AWS and learn quickly: What’s the maximum runtime for an Amazon Lambda function? When should I put my resources in an Amazon VPC? What’s the best container service to use to run my workload if I need to keep my costs low? How do I list my Amazon S3 buckets? How do I create and host a website on AWS? In the console, you can ask Amazon Q questions about AWS services, best practices, and even software development with the AWS SDKs and AWS CLI. Amazon Q in the console can generate short scripts or code snippets to help you get started using the AWS SDKs and AWS CLI. Conclusion Whether you have just started reading about the cloud, or have been using AWS for a decade, keeping pace with the advances in cloud is a continuous learning journey. The more streamlined the process to ask clarifying questions during reading or building, the more efficient this journey becomes. In service of this, Amazon Q can help cut down the time it takes to find the right documentation and get your questions answered. If you have an AWS account, you can start using Amazon Q on any public documentation or in the AWS Console today. AWS sees security as top priority and have integrated responsible AI into the development of services like Amazon Q. We adhere to the AWS Responsible AI policy and we expect users to follow the same code of conduct. View the full article

AWS Secrets Manager serves as a centralized and user-friendly solution for effectively handling access to all your secrets within the AWS cloud environment. It simplifies the process of rotating, maintaining, and recovering essential items such as database credentials and API keys throughout their lifecycle. A solid grasp of the AWS Secrets Manager concept is a valuable asset on the path to becoming an AWS Certified Developer. In this blog, you are going to see how to retrieve the secrets that exist in the AWS Service Manager with the help of AWS Lambda in virtual lab settings. Let’s dive in! What is a Secret Manager in AWS? AWS Secrets Manager is a tool that assists in safeguarding confidential information required to access your applications, services, and IT assets. This service makes it simple to regularly change, oversee, and access things like database credentials and API keys securely. Consider the AWS Secrets Manager example, users and applications can retrieve these secrets using specific APIs, eliminating the necessity of storing sensitive data in plain text within the code. This enhances security and simplifies the management of secret information. AWS Secrets Manager Pricing AWS Secrets Manager operates on a pay-as-you-go basis, where your costs are determined by the number of secrets you store and the API calls you make. The service is transparent, with no hidden fees or requirements for long-term commitments. Additionally, there is a 30-day AWS Secrets Manager free tier period, which begins when you store your initial secret, allowing you to explore AWS Secrets Manager without any charges. Once the free trial period ends, you will be billed at a rate of $0.40 per secret each month, and $0.05 for every 10,000 API calls. AWS Secrets Manager Vs Parameter Score What are AWS Lambda functions? AWS Lambda is a service for creating applications that eliminates the need to manually set up or oversee servers. AWS Lambda functions frequently require access to sensitive information like certificates, API keys, or database passwords. It’s crucial to keep these secrets separate from the function code to prevent exposing them in the source code of your application. By using an external secrets manager, you can enhance security and avoid unintentional exposure. Secrets managers offer benefits like access control, auditing, and the ability to manage secret rotation. It’s essential not to store secrets in Lambda configuration environment variables, as these can be seen by anyone with access to view the function’s configuration settings. Architecture Diagram for retrieving secretes in AWS Secrets Manager with AWS Lambda When Lambda invokes your function for the first time, it creates a runtime environment. First, it runs the function’s initialization code, which includes everything outside of the main handler. After that, Lambda executes the function’s handler code, which receives the event payload and processes your application’s logic. For subsequent invocations, Lambda can reuse the same runtime environment. To access secrets, you have a couple of options. One way is to retrieve the secret during each function invocation from within your handler code. This ensures you always have the most up-to-date secret, but it can lead to longer execution times and higher costs, as you’re making a call to the secret manager every time. There may also be additional costs associated with retrieving secrets from the Secret Manager. Another approach is to retrieve the secret during the function’s initialization process. This means you fetch the secret once when the runtime environment is set up, and then you can reuse that secret during subsequent invocations, improving cost efficiency and performance. The Serverless Land pattern example demonstrates how to retrieve a secret during the initialization phase using Node.js and top-level await. If the secret might change between invocations, make sure your handler can verify the secret’s validity and, if necessary, retrieve the updated secret. Another method to optimize this process is to use Lambda extensions. These extensions can fetch secrets from Secrets Manager, cache them, and automatically refresh the cache based on a specified time interval. The extension retrieves the secret from Secrets Manager before the initialization process and provides it via a local HTTP endpoint. Your function can then get the secret from this local endpoint, which is faster than direct retrieval from Secrets Manager. Moreover, you can share the extension among multiple functions, reducing code duplication. The extension takes care of refreshing the cache at the right intervention to ensure that your function always has access to the most recent secret, which enhances reliability. Guidelines to retrieve secrets stored in AWS Secrets Manager with AWS Lambda To retrieve the secrets retained in the AWS Secret Manager with the help of AWS Lambda, you can follow these guided instructions: First, you need to access the Whizlabs Labs library. Click on guided labs on the left side of the lab’s homepage and enter the lab name in the search lab tab. Now, you have found the guided lab for the topic you have entered in the search tab. By clicking on this lab, you can see the lab overview section. Upon reviewing the lab instructions, you may initiate the lab by selecting the “Start Lab” option located on the right side of the screen. Tasks involved in this guided lab are as follows: Task 1: Sign in to the AWS Management Console Start by accessing the AWS Management Console and set the region to N. Virginia a.You need to ensure that you do not edit or remove the 12-digit Account ID in the AWS Console. Copy your username and password from the Lab Console, then paste them into the IAM Username and Password fields in the AWS Console. Afterward, click the ‘Sign in’ button. Task 2: Create a Lambda Function Navigate to the Lambda service. Create a new Lambda function named “WhizFunction” with the runtime set to Python 3.8. Configure the function’s execution role and use the existing role named “Lambda_Secret_Access.” Adjust the function’s timeout to 2 minutes. Adjust the function’s timeout to 2 minutes. Task 3: Write a Lambda to Hard-code Access Keys Develop a Lambda function that creates a DynamoDB table and inserts items. This code will include hard-coded access keys. Download the code provided in the lab document. Replace the existing code in the Lambda function “WhizFunction” with the code from “Code1” in the downloaded zip file. Make sure to change the AWS Access Key and AWS Secret Access Key as instructed in the lab document. Deploy the code and configure a test event named “WhizEvent.” Run the test to create a DynamoDB table with i followed by configuration of the test event. Now click on the save button and click the test button to execute the code. The DynamoDB table was created successfully with some data fields. Task 4: View the DynamoDB Table in the Console Access the DynamoDB service by searching service in the top left corner. In the “Tables” section, you will find a table named “Whizlabs_stud_table1.” You can view the items within the table by selecting the table and clicking “Explore table items.” Task 5: Write a Lambda Code to Return Table Data Modify the Lambda function “WhizFunction” to write code that retrieves data from the DynamoDB table. Replace the existing code with the code from “Code2” in the lab document, making the necessary AWS Access Key and AWS Secret Access Key changes. Deploy the code and execute a test to enable the Lambda function to return data from the table. Task 6: Create a Secret Manager to Store Access Keys Access AWS Secrets Manager and make sure you are in the N. Virginia Region. Create a new secret by specifying it as “Other Type of Secret.” Enter the Access Key and Secret Access Key as key-value pairs. Choose the default encryption key. Name the secret “whizsecret” and proceed with the default settings. Review and store the secret and copy the Secret ARN for later use. Task 7: Write a Lambda to Create DynamoDB Items Using Secrets Manager Modify the Lambda function to create a new DynamoDB table and insert items by retrieving access keys from Secrets Manager. Replace the code with the code from “Code3” in the lab document, updating the Secret ARN. Deploy the code and run a test to create the DynamoDB table and items securely. Task 8: View the DynamoDB Table in the Console Access the DynamoDB service. In the “Tables” section, you will find a table named “Whizlabs_stud_table2.” To view the items, select the table and click “Explore table items.” Task 9: Write a Lambda Code to View Table Items Using Secrets Manager. Modify the Lambda function to write code that fetches table items securely using access and secret keys stored in Secrets Manager. Replace the code with the code from “Code4” in the lab document, updating the Secret ARN. Deploy the code and execute a test to securely access and view table items. Task 10: Cleanup AWS Resources Finally, you can delete the Lambda function “WhizFunction.” Delete both DynamoDB tables created. Delete the secret “whizsecret” from AWS Secrets Manager. Schedule its deletion with a waiting period of 7 days to ensure cleanup. Finally, end the lab by signing out from the AWS Management console. Also Read : Free AWS Developer Associate Exam Questions FAQs How much does the AWS Secret Manager parameter store cost? Parameter Store doesn’t incur any extra costs. However, there is a maximum limit of 10,000 parameters that you can store. What can be stored in AWS secrets manager? AWS Secrets Manager serves as a versatile solution for storing and managing a variety of sensitive information. This includes but is not limited to database credentials, application credentials, OAuth tokens, API keys, and various other secrets essential for different aspects of your operations. It’s important to note that several AWS services seamlessly integrate with Secrets Manager to securely handle and utilize these confidential data points throughout their entire lifecycle. What is the length limit for the AWS secrets manager? In the Secrets Manager console, data is stored in the form of a JSON structure, consisting of key/value pairs that can be easily parsed by a Lambda rotation function. AWS Secret manager limits range from 1 character to 65536 characters. Also, it’s important to note that the tag key names in Secrets Manager are case-sensitive. What are the benefits of AWS Secrets Manager? Secrets Manager provides a secure way to save and oversee your credentials. It makes the process of modifying or rotating your credentials easy, without requiring any complex code or configuration adjustments. Instead of embedding credentials directly in your code or configuration files, you can opt to store them safely using Secrets Manager. What is the best practice for an AWS secrets manager? You can adhere to the below listed AWS Secrets Manager best practices to carry out the secret storing in a better way: Make sure that the AWS Secrets Manager service applies encryption for data at rest by using Key Management Service (KMS) Customer Master Keys (CMKs). Ensure that automatic rotation is turned on for your Amazon Secrets Manager secrets. Also, confirm that the rotation schedule for Amazon Secrets Manager is set up correctly. Conclusion Hope this blog equips you with the knowledge and skills to effectively manage secrets within AWS, ensuring the protection of your critical data. Following the above AWS Secrets Manager tutorial steps can help to access the sensitive information stored in Secret Manager securely with the usage of AWS Lambda. You can also opt for AWS Sandbox to play around with the AWS platform. View the full article