15+ AWS Projects Ideas for Beginners to Practice in 2024

Explore some interesting guided AWS projects ideas for beginners with source code to practice that can be a value add-on to a resume in 2024.

15+ AWS Projects Ideas for Beginners to Practice in 2024
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AWS (Amazon Web Services) is the world’s leading and widely used cloud platform, with over 200 fully featured services available from data centers worldwide. This blog presents some of the most unique and innovative AWS projects from beginner to advanced levels. These AWS project ideas will give you a better idea of various AWS tools and their business applications.


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With over 1 million active enterprise customers, 8K AWS partner network members,1900+ third-party software products, and over 70 million hours spent on the Amazon Marketplace monthly by its customers - AWS is a name to reckon with in the cloud computing industry. Amazon Web Services was launched in July 2002 from the existing Amazon cloud platform with the initial purpose of managing online retail transactions. Today, AWS offers over 200 fully-featured services spread across 18 geographical regions. You can access these services for free or by paying nominal charges using an AWS Free Tier account. AWS generated revenue of $18 Billion in 2017, and the figure has been aggressively rising since then.  Before we get into the technicalities on how one can leverage any AWS service and build some exciting AWS projects, here is a quick overview of AWS to understanding the cloud platform and its services. 

 

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What is AWS?

AWS is a secure cloud computing platform that provides cloud computing, databases, networking, content storage, etc. AWS offers improved flexibility and security to the customers. The core infrastructure is formed to meet the security requirements for various applications developed using the services and the platform. As illustrated in the figure below, AWS offers a wide range of services classified into different types and categories. 

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Applications of AWS Projects 

AWS projects have a broad array of applications. It is possible to quickly develop and deploy AWS real-time projects for fundamental to advanced applications. Any cloud computing professional can design AWS cloud projects and AWS enterprise projects using these services. Amazon Elastic Compute Cloud, EC2, enables users to rent virtual computing resources to run their applications. AWS Lambda is another foundation service for serverless computing. AWS users can run code without worrying about managing the services or creating workload-aware cluster scaling logic. AWS Lambda projects are popular as they require zero administration. Internet of Things, IoT is one of the emerging technologies. AWS resources offer immense opportunities in AWS IoT projects as well. 

AWS has exceptional flexibility to select the desired operating system, database, and other services. The virtual environment provides the capabilities to load the services and the software as per the application needs. The migration process from an existing platform to an AWS-based solution is also easy—security, reliability, and ease of use are other attributes attached to AWS projects and applications. 

As a result, these projects are applicable in the academic phase and the professional journey. Students can work on these AWS project ideas to list them on their resumes and showcase their cloud computing skills to the recruiters to land their dream job. AWS provides ease of developing highly complex projects for industrial and business applications. 

Why should you work on AWS Projects?

AWS platform allows the user to use cloud computing models, such as Infrastructure as a Service, IaaS, Platform as a Service, PaaS, and Software as a Service, SaaS. Cloud Computing technologies are now an integral part of business processes and operations. The projects on AWS can assist in developing skills in cloud computing and the other advanced and significant technologies, such as IoT, AI, and many more. 

AWS projects for beginners can allow professionals to explore the various service offerings. They can also assist in developing and enhancing web development, hosting, design, and deployment skills. Data management and handling is another area that can be explored and improved by working on these project ideas. 

Many of the open-source projects on AWS are available and can provide a better understanding. Professionals can also enhance their skill-sets by working on these AWS freelance projects. 

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Top 15+ AWS Real-Time Projects Ideas for Practice in 2024

We have curated a list of exciting project ideas on AWS. You can work on these AWS sample projects to expand your skills and knowledge.  

AWS Projects Ideas

AWS Projects for Beginners/Freshers

This section lists some of the easiest AWS projects for beginners that will help you upskill yourself by working on various AWS cloud services.

1. Rapid Document Conversion 

The goal is to quickly and accurately convert the document to the desired format as selected by the user. Many document converters, such as PDF to word converters and others, are available online. You must have experienced the need to convert an HTML page/document into PDF format. Similarly, it is often required to convert an excel sheet to a word document or other formats. AWS Lambda will allow you to develop an app that can rapidly convert documents from one format to the other. You can retrieve the required content and can format and convert the content to download or display on the webpage. You can deploy such an app in a job portal wherein the users often wish to convert their resume to another format. 

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2. Windows Virtual Machine – Deployment 

The goal is to deploy Windows Virtual Machine with zero security violation instances in the process. VM Management in Microsoft Azure is a popular tool utilized to deploy virtual machines. You can deploy Windows VM in AWS, and for this purpose, you can use Amazon Lightsail as the web service. It will assist in simplifying the task and will enable you to utilize the optimum number of resources as per the need. The user interface offered with this service is easy to adapt to, and you can use the service to connect with the RDP client. 

3. Mass Emailing using AWS Lambda

This project aims to send mass e-mails to a business firm's existing and potential customers. MoonMail is one of the real-world mass mailing cloud platforms designed with the help of AWS Lambda.  To develop a cost-effective mass-mailing platform, you can combine AWS Lambda with Simple Email Service, SES. Along with S3, you can share mass mails with more recipients. 

Mass Emailing using AWS Lambda & SES

As soon as you upload a CSV file, it will trigger an S3 event. Lambda function will then import the file into the database. The process of sending the mail to the addresses provided will begin. 

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4. Website Development using AWS

The goal is to develop a website with the help of AWS with high security, reliability, usability, and availability. AWS Lightsail is the virtual private server for creating numerous websites. You can experiment with working on AWS by creating a website. You may create a website to store the student details in a university ay develop the website to track your home expenses. You can use AWS EC2 or AWS Lambda services with AWS Lightsail as the virtual private server. It will provide SSD-based storage and comes pre-configured with several web development options. 

5. Serverless Web App 

The goal is to develop and deploy a serverless web app that is secure and usable with the help of AWS (Amplify, Lambda, etc.).  Netflix is a popular real-world application that uses many AWS and cloud services. You can combine numerous technologies to work on the project. AWS Amplify, for instance, will be needed for the front-end of the app along with hosting processes. AWS Cognito can manage the authentication and administration for the back-end. You can also use DynamoDB to incorporate a persistence layer for storage. AWS Lambda and API Gateway are suitable for using the backend API. You shall also be aware of HTML, JavaScript, CSS, and RESTful API implementation. You can begin with a simple app, such as a MI calculator. 

Intermediate Level AWS Projects

Once you've worked on the beginner-level AWS projects, it's time for you to test your progress by trying your hands-on few intermediate-level AWS projects. These projects are mainly suitable for working professionals having around two to four years of working experience.

6. Real-time Data Processing Application 

The goal is to process the high-volume data quantities in real-time with no compromises on the accuracy of the outcomes. Bustle is a real-world example that processes massive volumes of site metric data in real-time by leveraging AWS resources. You can use Amazon Kinesis Stream and AWS Lambda to work on this project. You will be required to create a Kinesis Stream in the initial step, and it will be essential for you to configure it to capture the data from a web source. Several Lambda function instances will be scaled up or down automatically with the scaling of the stream. You can use social media timelines or location-based data as your data sources. 

Real Time Data Processing Example using AWS Lambda

Image Credit:  www.simform.com 

You can integrate Kinesis and AWS Lambda in either three formats: a stream-based model, synchronous invocation model, or event structure model. 

7. Customer Logic Workflow

The goal is to design and deploy custom logic workflows for the applications in response to the trigger events.  Coke vending machine is a real-world application of AWS Lambda and SNS. Food Panda, a food delivery app, is another famous example that implements this.  It is now possible to include Lambdas in the existing workflows with step functions. It is an AWS project for beginners because these functions will be short, and you can quickly test and validate the outcome. Shopping cart management is one of the areas wherein you can design and implement this project. The information will be readily available from any of the e-commerce websites. 

8. Kubernetes Clusters on Amazon EC2 Spot

The project aims to set up Kubernetes clusters on Amazon EC2 Spot with 100% adherence to the best practices. Kubernetes is open-source and extremely popular in the cloud computing industry with abundant real-world applications. It is an excellent AWS project for beginners developing AWS and Kubernetes skills. Amazon EC2 is one of the foundational services of AWS, and you will have to work on the same. The service gets dynamic computing capabilities on the cloud. You shall take a step ahead and use Amazon EC2 Spot instances for this project. These instances and Kubernetes follow the same approach towards containers, so you will have the option to use both of them. In this project, you can build multiple node groups. Also, you shall focus on capacity optimization for allocation. It will make sure that the functions of the worker nodes are adequate. 

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9. Content Recommendation System

The goal is to use AI and ML with AWS to recommend the content to the end-users based on the history.  Almost all streaming apps, such as Netflix or Amazon Prime, have content recommendation systems. You can use AWS cloud with nearest neighbor algorithms to work on this project. For this project,  use Amazon SageMaker. It is the tool for carrying out ML implementations with ease. It includes built-in algorithms that do not need label data. Also, it uses semantic search in place of string matching to simplify the tasks. AWS combined with nearest neighbor algorithms will provide accurate results and recommendations. 

10. Chatbots using AWS Lex

The project’s goal is to develop a chatbot to provide instant replies to the messages sent by the users. Amazon and many other e-commerce android apps have chatbots installed to reply to users’ queries instantly. You can use Amazon Lex to build a chatbot and combine it with AWS Lambda for exceptional outcomes. Lex is the service specifically developed to simplify chatbot development. You can use the same to experience one-click deployment and add your application to the desired platform. Platform independence is also essential for the chatbots created using Amazon Lex. 

Advanced Level AWS Projects for Practice 

This section includes some advanced-level projects suitable for professionals who are willing to further strengthen their skillset by working on industry-relevant projects.

11. Custom Alexa Skills

The goal is to develop a virtual assistant replicating the skills and functions of Amazon Alexa. Alexa is a product by Amazon and is a widely used virtual assistant.  You can use AWS Lambda with a custom Alexa skill set, and it is an object embedded within AWS Console to invoke the handler function. Along with this, use the Alexa skill handler function, which is an AWS Lambda function. You will obtain the custom logic, and it will manage the fulfillment of the user’s request. You can also use third-party functions hosted outside of the Alexa skill. You can begin with the basic tasks, such as playing the music or creating a reminder. 

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12. Serverless Image Recognition Engine 

The goal is to develop an application that can automatically recognize the images uploaded on the app. NatGeo image recognition is an example of a widely used real-world app. You can use AWS Lambda and several in-built functions to develop this machine learning project. For example, one of the Lambda functions will invoke the metadata in the image uploaded. The other will gather Rekognition to identify the patterns in the picture. You shall also use DynamoDB to maintain the back-end of the application. 

13. Text-to-Speech Converter

This machine learning project aims to develop an app that can convert text to speech. Text-to-Speech is an AI-based functionality witnessed in many websites and web apps. Google Text-to-Speech is one of the popularly used applications. With AWS Lambda & Amazon Polly, you can convert textual information to speech. The combination can provide you with the ability to develop lifelike speech synthesis applications. With Amazon Polly, you can use advanced deep learning technologies to carry out accurate conversions. AWS Lambda will provide the ability to improve the response rate as it will be critical in any of the real-time applications. 

14. Personalized News Feed

The goal is to create a personalized news feed based on the preferences and previous search and browsing history. Google uses this functionality to show the suggested articles in the mobile browser based on the search and browsing history. You can use AWS DynamoDB and AWS Lambda to develop a personalized content delivery platform. You will be required to extract the information from user touchpoints. DynamoDB stores the information for the application. Data stored and Lambda functions are the platforms to develop the user profiles. Associated parameters enable the creation of the customer feed. 

15. Blood Bank Management System

The project's goal is to develop a web app for accurately managing the blood bank. Many city blood banks now use cloud-based platforms to keep track of the blood units available or needed.  You can use AWS EC2 and AWS DynamoDB as the services to develop this application. In this project, you can create a simple UI to enable the users to view the blood units available in the blood bank, book the requirements, and book the donation to the blood bank.DynamoDB stores the data at the back-end. 

16. Orchestrate Redshift ETL using AWS Glue and Step Functions

Amazon began offering its cloud computing services in 2006. And since then, it hasn’t stopped adding exciting features to its product for its valuable customers. One such feature is the Redshift which can be used as a data warehousing tool. In this project, you will understand how to build an ETL (Extract, Transform, and Load) Big Data pipeline with the help of AWS tools and in-house featured applications for drawing relevant business insights from the data.

Data Description

The Amazon eCommerce website hosts various products from various sellers and thus has a massive dataset of its customers' reviews. This dataset is now a valuable asset for machine learning, Natural language processing, and deep learning applications. In this project, you will be working on the Amazon Customer Reviews dataset that contains product reviews written by Amazon customers between the years 1995-2015. There are about 200K+ reviews from customers from five different countries. You can download the dataset in two formats: TAV (Tab-separated values)/ Parquet (an optimized columnar binary format).

Services- Amazon Redshift, Amazon Glue, AWS Step Function, VPC, QuickSight

Language Used - Python3, SQL

Packages/Libraries - Boto3, Sys

Source Code- Orchestrate Redshift ETL using AWS Glue and Step Functions

AWS Projects for Portfolio/Resume 

The most effective strategy to earn a hiring manager's trust is to showcase your ability to perform the tasks they need. In the Big data industry, this strategy entails building a solid portfolio. How can you do that? Well, add a good number of projects to your Big Data portfolio. This section includes some unique industry-relevant AWS project ideas for beginners. Working on these projects will make you stand out from the rest of your competitors and help you land your dream job.

17. Building Real-Time AWS Log Analytics Solution

Log analytics, a typical Big Data use-case, enables you to monitor application availability, detect fraud, and manage service level agreements. In this AWS project, you will create an end-to-end log analytics solution to gather, ingest, and analyze data. Once the data analysis is over, it will allow you to assess the status of production systems on AWS. Working on this real-time AWS project will allow you to explore various AWS native services such as Amazon S3, AWS IAM, AWS EC2, AWS Glue, etc.

Source Code- Building Real-Time AWS Log Analytics Solution

18. Website Monitoring using AWS Lambda and Aurora

Website Monitoring validates that the website is fully operational and that web users can navigate the site without difficulty. For this real-time AWS project, you will leverage AWS tools such as Amazon Dynamo DB, Lambda, Aurora, MySQL, and Kinesis to put together optimum solutions for website monitoring. Create an AWS EC2 instance and install Amazon Kinesis on it. Then, with Amazon Kinesis, build data analytics streams for real-time data streaming. Then, after launching AWS EC2 instances, assign the EC2 instance an Identity Access Management (IAM) role. Create Kinesis Analytics for performing log streaming on Kinesis data streams. Use AWS Lambda to load order logs into Amazon DynamoDB, and then use Kinesis Analytics to stream the data in real-time.

Source Code- Website Monitoring using AWS Lambda and Aurora

19. Build an Analytical Platform for eCommerce using AWS Services

aws sample projects

Ecommerce analytics gathers information from all of the factors that impact a retail outlet. Analysts can then use this data to infer changes in customer behavior and online shopping trends. In this project, you will use an eCommerce dataset to develop two analytical pipelines: batch and real-time, based on logs of user purchases, product views, cart history, and the user's path on the digital platform. This project requires you to perform batch processing that entails ingesting data using S3, processing the data with Amazon Glue, and visualization using Amazon Kinesis. Use AWS QuickSight to draw relevant business insights from the data.

Source Code- Build an Analytical Platform for eCommerce using AWS Services

AWS Projects on GitHub

Github offers various projects and repositories that help students and working professionals enhance their skill set by practicing those projects. You will find several open-source projects with source code on Github that you can try your hands on. Below are a few exciting AWS project ideas for beginners that are available on Github-

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20. Hybrid Recommendation System

innovative aws projects

In this project, you will use the Yelp dataset to perform a high-level data analysis to generate hotel recommendations. The project mainly focuses on designing a hybrid system that combines content-based, collaborative-based, and social networking-based systems. The tech stack for this machine learning project includes Apache Spark, MongoDB, AWS - EC2, EMR, and Java.

Github link- Hybrid Recommendation System

21. Forest Wildfire Analytics

Wildfire prediction is an intelligent approach to preventing and minimizing wildfires. Wildfire predictions enable you to quickly analyze the source of the fire and determine the best course of action. Create a dynamic, efficient, and automatic machine learning workflow with AWS Cloud for this wildfire analytics project. Create functions with AWS Lambda and a Redshift cluster to interact with AWS Lambda and AWS Glue. Storing the input CSV file in the S3 bucket helps trigger the Lambda function. You can use the AWS Sagemaker Python SDK to prepare and train your machine learning model on the data. Use Quicksight to create wildfire data visualizations and interactive dashboards after executing queries on the data with AWS Athena and Glue crawlers.

Github link- Forest Wildfire Analytics

22. Sentiment Analysis on Real-time Twitter Data

This AWS project aims to create a system that can evaluate the sentiment of all real-time Tweets using a specified Twitter hashtag. Use a basic Python script to obtain real-time Twitter data. Put the Tweets directly into a Kinesis Firehose delivery stream with a transformation Lambda function from the script. Use Amazon Comprehend to get sentiment data and a cleaned/processed Twitter comment. Finally, an Elasticsearch domain saves the Tweet and its sentiment data, where custom charts will display real-time data.

Github link- Sentiment Analysis on Real-time Twitter Data

23. Object Detection using AWS Sagemaker

Using the TensorFlow Object Detection API and Amazon SageMaker, this existing git repository involves building, training, and deploying an EfficientDet model. It leverages TensorFlow 2 to make developing, training and deploying object detection models simple. It also features the TensorFlow 2 Detection Model Zoo, a library of pre-trained detection models that speeds up your project. You will train a Tensorflow Object Detection model to detect bees from RGB images using an example dataset from inaturalist.org. This project will show you how to use SageMaker Processing to create the TFRecords dataset and label map. You will learn how to use TF2 on Amazon SageMaker to fine-tune an EfficientDet model. Working on this project will also teach you how to use Tensorboard and the SageMaker Debugger to track your machine learning model training.  The project's final step is to deploy your model on a SageMaker Endpoint and visualize the results.

Github link- Object Detection using AWS Sagemaker

Python AWS Projects

When working with specific AWS resources, Python is one of the most popular choices. One of the reasons causing this popularity is that using Python SDK 'Boto' in AWS allows for efficient development with less code. If you wish to learn more about how Python makes AWS easier to use, here are a few AWS project ideas for beginners worth exploring.

24. AWS Athena Big Data Project for Querying COVID-19 Data

top aws projects

This is one of the easiest AWS projects you can practice. You will learn to examine the covid-19 dataset using AWS Athena, a serverless SQL query engine. The Covid-19 database comprises Covid-related timestamps, posts, and comments. The project will teach you how to leverage Amazon Glue to create tables and offers you a chance to explore various Athena joins. In the AWS Glue Data Catalog, you will use Python to create tables using crawlers. Working on this project will also help you understand how AWS Athena pricing varies depending on the file size. Additionally, this project involves a few other services such as Amazon S3, Amazon CloudWatch, etc. AWS S3 allows you to store the dataset (CSV file) in S3 buckets for further processing, and CloudWatch keeps track of your data's log files and lets you analyze them as needed. 

Source Code- AWS Athena Big Data Project for Querying COVID-19 Data

25. Build an AWS ETL Data Pipeline in Python on YouTube Data

Cloud-based security Data Lake solutions help to generate rich analytics on data by categorizing it into multiple storage segments, such as raw, cleaned, and analytical. This project intends to organize, simplify, and analyze structured and semi-structured YouTube video data based on video genres and trending metrics securely and efficiently. It contains data (in CSV files) on the YouTube videos trending daily for several months.

Source Code- Build an AWS ETL Data Pipeline in Python on YouTube Data

26. Build a Real-Time Streaming Data Pipeline using Flink and Kinesis

advanced aws projects

Real-time data gives you the information you seek quickly and in context, allowing you to make highly informed business decisions. This AWS big data project will teach you how to use Amazon Kinesis to host an Apache Flink Python program for a real-time streaming platform. This project involves simulating real-time accident data and building a pipeline that will allow you to analyze the situation and take timely measures. Working on this project will help you better understand various services such as AWS Kinesis, Apache Flink, Grafana, and Amazon SNS.

Source Code- Build a real-time Streaming Data Pipeline using Flink and Kinesis

Jump-Start Your Cloud Career with Real-Time Hands-On AWS Projects

AWS sample project ideas listed above will introduce a broad range of capabilities you can explore with AWS services. You can also use these ideas to enhance your AWS cloud platforms and frameworks skill-set to land a top gig as a AWS cloud computing professional. Successful outcomes in these projects will inspire you to develop self-motivated projects on AWS. You can implement your ideas to build useful industrial and business apps using AWS services. You will gain experience in cloud technologies with other latest technological concepts. Artificial Intelligence and Big Data are some of the technologies with active involvement in many project ideas. You will acquire additional analytical, problem-solving, and risk resolution skills with hands-on experience by working on these AWS projects. 

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FAQs on AWS Projects

  • Build an AWS ETL Data Pipeline in Python on YouTube Data- This AWS project entails organizing, streamlining, and analyzing structured and semi-structured YouTube video data based on video categories and trending metrics in a secure manner. Use the YouTube trending video dataset from Kaggle, which contains statistics (CSV files) on popular YouTube videos daily for several months.

  • AWS Snowflake Data Pipeline Example using Kinesis and Airflow- In this project, you will build a data pipeline that starts with EC2 logs and ends with Snowflake and S3 post-transformation and Airflow DAGs processing. Use two different data files (for example, customers and orders) to implement this project.

  • AWS Project-Website Monitoring using AWS Lambda and Aurora- This AWS project entails real-time monitoring of webpages by employing various AWS services like Lambda, Aurora MySQL, Amazon Dynamo DB, and Kinesis to monitor your website.

  • Step 1- Log in to the AWS Management Console and go to console.aws.amazon.com to access the Amazon Pinpoint console.

  • Step 2- It will take first-time users to a page that walks them through the platform's functions. Choose Create a project from the Get started section after giving your project a name. Existing users choose the option Create a project from the All projects page and enter a name for your project in the Project name box.

  • Step 3- Choose Configure next to Email on the Configure features page.

  • Step 4- Fill in the Email address field with the email address for user authentication (personal email address or work email address) you want to send emails. Then, select Verify.

  • Step 5- You will receive an email from Amazon Web Services with the subject line "Amazon Web Services – Email Address Verification Request in region RegionName," where RegionName is the name of the AWS Region in which Amazon Pinpoint is configured.

  • Step 7- After opening the email, click the link in the email's body.

  • Step 8- Head back to the Amazon Pinpoint console in your browser. Choose Save on the Setup email page.

  • Is AWS easy to learn?

Yes, AWS is easy to learn. Learning AWS is easy if you have a good understanding of the fundamentals of cloud computing. Referring to AWS documentation, YouTube videos, and working on hands-on real-time AWS projects are some of the best ways to learn AWS. 

You will find various unique and exciting AWS projects for practice on ProjectPro, Github, etc.

Setting up the AWS Account on the Free Tier is one of the best ways to practice AWS projects. Customers can use it to try out AWS services for free, up to the limits set for each service.

If you put in at least 2 to 3 hours each day and have fundamental IT and networking abilities, it will take 2 to 3 months to learn AWS.

You can learn AWS from scratch with the freely-accessible AWS Training center. You can proceed further by attending AWS events, webinars, workshops, etc. You can check out Youtube tutorials and free AWS courses. Once you have gained sufficient theoretical knowledge, it's time to put them into practice. You will find some interesting industry-relevant AWS projects on ProjectPro, Github, etc.

7. How can I take up Open AWS Projects?

To take up open AWS projects, you can start by joining online communities and forums related to AWS. There, you can find open-source projects that need contributors. You can also search for GitHub repositories that use AWS services and contribute to them. Additionally, you can participate in hackathons and online coding competitions that involve AWS technologies.

8. How to build a Portfolio for AWS Projects?

To build a portfolio for AWS projects, start by identifying projects that align with your interests and skills. Build small applications that demonstrate your ability to use AWS services such as EC2, S3, and Lambda. Document your projects with code samples, screenshots, and brief descriptions. Publish your portfolio on platforms like GitHub or LinkedIn to showcase your skills to potential employers.

9. What are Some Small Projects using AWS that Will Be Good for Resume?

Here are some small projects using AWS that would be good for a resume:

  1. Build a serverless web application using AWS Lambda and API Gateway.

  2. Create a static website using AWS S3 and CloudFront.

  3. Set up a continuous deployment pipeline using AWS CodePipeline and CodeDeploy.

  4. Develop a chatbot using AWS Lex and Lambda.

  5. Implement a database using AWS DynamoDB and Lambda.

10. What are Some Best Practices for AWS Project Development and Deployment?

Here are some best practices for AWS project development and deployment:

  1. Use infrastructure as code to manage your AWS resources and configurations.

  2. Implement security best practices, including least privilege access and encryption.

  3. Test your applications thoroughly before deployment using automated testing.

  4. Monitor your AWS resources using CloudWatch and implement automated alerting.

  5. Use a version control system for your code and infrastructure to track changes and collaborate with your team.

 

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