Further reading

The following posts contain detailed walkthroughs and sample code for building the components of the serverless data lake centric analytics architecture:

• Load ongoing data lake changes with AWS DMS and AWS Glue

• Discover metadata with AWS Lake Formation: Part 1 and Part 2

• Build a Data Lake Foundation with AWS Glue and Amazon S3

• Process data with varying data ingestion frequencies using AWS Glue job bookmarks

• Orchestrate Amazon Redshift-Based ETL workflows with AWS Step Functions and AWS Glue

• Analyze your Amazon S3 spend using AWS Glue and Amazon Redshift

• From Data Lake to Data Warehouse: Enhancing Customer 360 with Amazon Redshift Spectrum

• Extract, Transform and Load data into S3 data lake using CTAS and INSERT INTO statements in Amazon Athena

• Derive Insights from IoT in Minutes using AWS IoT, Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight

• Our data lake story: How Woot.com built a serverless data lake on AWS

• Predicting all-cause patient readmission risk using AWS data lake and machine learning