Module 1: Project 1: Building Serverless ETL Pipelines with AWS Glue
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1.1 Create S3 Buckets and Upload Sample Data 4
1.2 Create Glue Database and Crawler 5
1.3 Create and Run the ETL Job 6
1.4 Verify Results and Cleanup Module 2: Project 2: Visual Data Cleaning with AWS Glue DataBrew
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2.1 Create S3 Bucket and Upload Sample Data 3
2.2 Create DataBrew Dataset and Profile Data 4
2.3 Create and Apply Data Cleaning Recipe Module 3: Project 3: Building Real-time Streaming Pipelines with Amazon Kinesis
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3.1 Create Kinesis Stream and S3 Bucket 3
3.2 Create Stream Processing Lambda 4
3.3 Send Test Events and Monitor 5
3.4 Verify Results and Cleanup Module 4: Project 4: Building a Cloud Data Warehouse with Redshift Serverless and Spectrum
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4.1 Create Redshift Serverless and Load Data 3
4.2 Create Tables and Optimize Schema 4
4.3 Run Analytics Queries 5
4.4 Use Redshift Spectrum and Cleanup Module 5: Project 5: Orchestrating Data Workflows with AWS Step Functions
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5.1 Create Lambda Functions for Pipeline Steps 3
5.2 Create SNS Topics for Notifications 4
5.3 Create the Step Functions State Machine 5
5.4 Test the Pipeline End-to-End 6
5.5 View Execution History and Cleanup 
