AWS Data Engineering Labs

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Data engineering labs on AWS Glue, Athena , Lambda, Kinesis, S3, Redshift, EventBridge , EMR , Step functions and more

What you will learn

Enhance your data engineering skills on AWS with hands-on labs

Gain practical experience with essential AWS services like Glue, Lambda, Kinesis, S3, Redshift, and EventBridge

Learn how data catalogs, running ETL jobs, and orchestrating workflows solve real-world data engineering problems.

Gain practical experience to aid in your preparation for the Data Engineering certification (DEA-C01)

Description

This hands-on course is designed for individuals familiar with AWS to enhance their skills in data engineering. Students should have a basic understanding of Python, SQL, and database concepts. However, even beginners to data engineering can follow along and learn. The course is minimal on theory, focusing instead on practical aspects of data engineering on AWS. Participants will gain practical experience through a series of labs covering essential AWS services such as Glue, Lambda, Kinesis, S3, Redshift, EventBridge, and more. While the labs provide practical exercises, participants are encouraged to refer to AWS documentation for a full understanding of concepts. This course will also give you practical experience to aid in your preparation for the Data Engineering certification (DEA-C01)

AWS Data Engineering Labs :


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  • Creating a data catalog in Glue and viewing data in Athena
  • Running an ETL job using Glue
  • Triggering SNS Notification for S3 Upload Event using EventBridge
  • Orchestrating Lambda functions with Step Functions State Machine
  • ETL Workflow Orchestration with AWS Glue Lambda EventBridge Step Functions
  • Storing and Retrieving Data from a Kinesis Data Stream Using AWS CLI
  • Kinesis Data Stream Python Boto3 Producer & Consumer
  • Writing simulated weather data from a Kinesis Stream to S3 with AWS Lambda
  • Running Spark transformation jobs using Amazon EMR on EC2
  • Creating a Data Warehouse on S3 data using Amazon Redshift

Prerequisites:

  • Basic understanding of AWS
  • Basic knowledge of Python, SQL, Spark and database concepts

Note: Even if you are a beginner to data engineering, you can still follow and learn from this course.

English
language

Content

Introduction

Introduction

Orchestrating a Data Pipeline using Glue, Athena, EventBridge and Step Functions

Understanding AWS Glue
Lab – Creating a data catalog in Glue and viewing data in Athena
Lab – Running an ETL job using Glue
Understanding Amazon EventBridge
Lab – Triggering SNS Notification for S3 Upload Event using EventBridge
Understanding AWS Step Functions
Lab – Orchestrating Lambda functions with Step Functions State Machine
Lab – ETL Workflow Orchestration with AWS Glue Lambda EventBridge Step Functions

Building streaming Data Pipeline with Kinesis and Lambda

Understanding Kinesis Data Stream
Lab – Storing and Retrieving Data from a Kinesis Data Stream Using AWS CLI
Lab – Kinesis Data Stream Python Boto3 Producer & Consumer
Lab – Writing simulated weather data from a Kinesis Stream to S3 with AWS Lambda

Running Big Data workloads using Amazon EMR

Understanding Amazon EMR
Lab – Running Spark transformation jobs using Amazon EMR on EC2

Creating a Data Warehouse on Amazon Redshift

Understanding Amazon Redshift
Lab – Creating a Data Warehouse on S3 data using Amazon Redshift

Conclusion and where to go from here?

Conclusion
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