Master AWS Lambda Functions for Data Engineers using Python

Description

Do you need to be taught AWS Lambda Functions by constructing an end-to-end knowledge pipeline using Python as Programming Language and different key AWS Companies reminiscent of Boto3, S3, Dynamodb, ECR, Cloudwatch, Glue Catalog, Athena, and so forth? Right here is one course using which you’ll be taught AWS Lambda Functions by implementing an end-to-end pipeline by using all of the providers talked about.

As a part of this course, you’ll learn to develop and deploy lambda features using the zip information, customized docker pictures in addition to layers. Additionally, you’ll perceive the way to set off lambda features from Eventsbridge in addition to Step Functions.

• Arrange required instruments on Home windows to develop the code for ETL Data Pipelines using Python and AWS Companies. You’ll maintain organising Ubuntu using wsl, Docker Desktop, and Visible Studio Code together with Distant Growth Extension Package so that you could develop Python-based purposes using AWS Companies.

• Setup Undertaking or Growth Atmosphere to develop purposes using Python and AWS Companies on Home windows and Mac.

• Getting Began with AWS by creating an account in AWS and in addition configuring AWS CLI in addition to Overview Data Units used for the venture

• Develop Core Logic to Ingest Data from supply to AWS s3 using Python boto3. The appliance will probably be constructed using Boto3 to work together with AWS Companies, Pandas for date arithmetic, and requests to get the information from the supply through REST API.

• Getting Began with AWS Lambda Functions using Python 3.9 Run-time Atmosphere

• Refactor the appliance, and construct a zipper file to deploy as AWS Lambda Operate. The appliance logic consists of capturing bookmarks in addition to Job Run particulars in Dynamodb. Additionally, you will get an overview of Dynamodb and the way to work together with Dynamodb to handle Bookmark in addition to Job Run particulars.

• Create AWS Lambda Operate using a Zip file, deploy using AWS Console and Validate.

• Troubleshoot points associated to AWS Lambda Functions using AWS Cloudwatch

• Construct a customized docker picture for the appliance and push it to AWS ECR

• Create AWS Lambda Operate using the customized docker picture in AWS ECR after which validate.

• Get an understanding of AWS s3 Occasion Notifications or s3-based triggers on Lambda Operate.

• Develop one other Python utility to rework the information and in addition write the information within the type of Parquet to s3. The appliance will probably be constructed using Pandas by changing 10,000 data at a time to Parquet.

• Construct orchestrated pipeline using AWS s3 Occasion Notifications between the 2 Lambda Functions.

• Schedule the primary lambda perform using AWS EventsBridge after which validate.

• Lastly, create an AWS Glue Catalog desk on the s3 location which has parquet information, and validate by working SQL Queries using AWS Athena.

• After going by means of the entire life cycle of Deploying and Scheduling Lambda Operate and in addition validating the information by using Glue Catalog and AWS Athena, additionally, you will perceive the way to use Layers for Lambda Operate.

Listed here are the important thing takeaways from this coaching:

• Develop Python Functions and Deploy as Lambda Functions by using a Zip-based bundle in addition to a customized docker picture.

• Monitor and troubleshoot the problems by going by means of Cloudwatch logs.

• The complete utility code used for the demo together with the pocket book used to give you core logic.

• Means to construct options using a number of AWS Companies reminiscent of Boto3, S3, Dynamodb, ECR, Cloudwatch, Glue Catalog, Athena, and so forth