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via Udemy |
Go to Course: https://www.udemy.com/course/mastering-aws-lambda-functions/
Certainly! Here's an in-depth review and recommendation for the Coursera course on AWS Lambda Functions: --- ### Course Review and Recommendation: AWS Lambda Functions & Data Pipelines with Python Are you looking to master AWS Lambda and build sophisticated, end-to-end data pipelines using Python? This Coursera course offers a comprehensive, hands-on learning experience that covers the essentials of developing and deploying AWS Lambda functions integrated with key AWS services. ### What the Course Offers: **Hands-On Learning with Real-World Projects** The course skillfully guides you through building a complete data pipeline beginning with data ingestion, processing, and storage, to querying and validation. You'll learn to develop Python applications that interact with services like S3, DynamoDB, ECR, CloudWatch, Glue Catalog, Athena, and EventBridge, providing a robust understanding of cloud-based data workflows. **Diverse Deployment Techniques** You will explore various deployment methods including creating zip files, using custom Docker images, and employing Lambda Layers. This versatility is critical for deploying scalable and efficient serverless applications. **Practical Environment Setup** The course includes step-by-step instructions to set up a professional development environment on Windows (including WSL, Docker Desktop, and Visual Studio Code) as well as on Mac. This ensures you can develop and test your solutions locally before deploying to AWS. **Building and Managing Lambda Functions** Learn to develop Lambda functions using Python 3.9, refactor code into zip files, and deploy via the AWS Console. The course emphasizes troubleshooting with CloudWatch, which is vital for maintaining production-level applications. **Containerized Deployment** One standout feature is creating custom Docker images for Lambda, pushing these to AWS ECR, and deploying from container images—an increasingly popular approach for deploying complex applications. **Data Transformation & Orchestration** You will build a pipeline that ingests data into S3, transforms it into Parquet format using Pandas, and triggers subsequent processing steps with EventBridge and S3 event notifications. This holistic approach provides valuable insights into orchestrated serverless data workflows. **Data Querying & Visualization** Finally, the course covers creating Glue Catalog tables and querying data with Athena, enabling you to perform efficient serverless analytics on your datasets. ### Why I Recommend This Course: - **Extensive Coverage:** It provides both foundational and advanced topics, suitable for beginners and those looking to deepen their serverless AWS expertise. - **Practical Focus:** The project-based approach ensures you gain experience in deploying real-world solutions, not just theoretical knowledge. - **Tooling & Environment Setup:** Preparing local environments on Windows and Mac equips you with skills to develop efficiently and troubleshoot issues. - **Comprehensive Skill Development:** From Python development to AWS service integration, monitoring, troubleshooting, and data analytics, this course covers the entire lifecycle of serverless data pipelines. ### Final Verdict: If you want a thorough, practical course that teaches you how to build, deploy, and manage AWS Lambda functions within complex data workflows using Python—this course is highly recommended. It’s particularly suitable for data engineers, developers, and cloud enthusiasts eager to leverage AWS services for scalable, serverless data solutions. --- **Enroll now** to elevate your AWS Lambda and data pipeline skills with this thoughtfully structured course!
Do you want to learn AWS Lambda Functions by building an end-to-end data pipeline using Python as Programming Language and other key AWS Services such as Boto3, S3, Dynamodb, ECR, Cloudwatch, Glue Catalog, Athena, etc? Here is one course using which you will learn AWS Lambda Functions by implementing an end-to-end pipeline by using all the services mentioned.As part of this course, you will learn how to develop and deploy lambda functions using the zip files, custom docker images as well as layers. Also, you will understand how to trigger lambda functions from Eventsbridge as well as Step Functions.Set up required tools on Windows to develop the code for ETL Data Pipelines using Python and AWS Services. You will take care of setting up Ubuntu using wsl, Docker Desktop, and Visual Studio Code along with Remote Development Extension Kit so that you can develop Python-based applications using AWS Services.Setup Project or Development Environment to develop applications using Python and AWS Services on Windows and Mac.Getting Started with AWS by creating an account in AWS and also configuring AWS CLI as well as Review Data Sets used for the projectDevelop Core Logic to Ingest Data from source to AWS s3 using Python boto3. The application will be built using Boto3 to interact with AWS Services, Pandas for date arithmetic, and requests to get the files from the source via REST API.Getting Started with AWS Lambda Functions using Python 3.9 Run-time EnvironmentRefactor the application, and build a zip file to deploy as AWS Lambda Function. The application logic includes capturing bookmarks as well as Job Run details in Dynamodb. You will also get an overview of Dynamodb and how to interact with Dynamodb to manage Bookmark as well as Job Run details.Create AWS Lambda Function using a Zip file, deploy using AWS Console and Validate.Troubleshoot issues related to AWS Lambda Functions using AWS CloudwatchBuild a custom docker image for the application and push it to AWS ECRCreate AWS Lambda Function using the custom docker image in AWS ECR and then validate.Get an understanding of AWS s3 Event Notifications or s3-based triggers on Lambda Function.Develop another Python application to transform the data and also write the data in the form of Parquet to s3. The application will be built using Pandas by converting 10,000 records at a time to Parquet.Build orchestrated pipeline using AWS s3 Event Notifications between the two Lambda Functions.Schedule the first lambda function using AWS EventsBridge and then validate.Finally, create an AWS Glue Catalog table on the s3 location which has parquet files, and validate by running SQL Queries using AWS Athena.After going through the complete life cycle of Deploying and Scheduling Lambda Function and also validating the data by using Glue Catalog and AWS Athena, you will also understand how to use Layers for Lambda Function.Here are the key takeaways from this training:Develop Python Applications and Deploy as Lambda Functions by using a Zip-based bundle as well as a custom docker image.Monitor and troubleshoot the issues by going through Cloudwatch logs.The entire application code used for the demo along with the notebook used to come up with core logic.Ability to build solutions using multiple AWS Services such as Boto3, S3, Dynamodb, ECR, Cloudwatch, Glue Catalog, Athena, etc