What this book covers
Chapter 1, Data Management – Introduction and Concepts, introduces basic concepts associated with data management.
Chapter 2, Introduction to Important AWS Glue Features, introduces some important AWS Glue features.
Chapter 3, Data Ingestion, describes how to ingest data across multiple data stores.
Chapter 4, Data Preparation, describes typical data preparation use cases with both a GUI-based approach and a source code-based approach using AWS Glue.
Chapter 5, Designing Data Layouts, describes how to optimize data layout on Amazon S3 using AWS Glue.
Chapter 6, Data Management, describes how to manage, clean up, and enrich data using AWS Glue.
Chapter 7, Metadata Management, describes how to populate and maintain metadata based on data using AWS Glue.
Chapter 8, Data Security, describes how to secure your data by access control, encryption, auditing, and network security using AWS Glue.
Chapter 9, Data Sharing, describes how to share your data across multiple accounts to democratize your data lake.
Chapter 10, Data Pipeline Management, describes how to build and orchestrate a data-processing pipeline using AWS Glue.
Chapter 11, Monitoring, describes how to monitor a data lake and AWS Glue components.
Chapter 12, Tuning, Debugging, and Troubleshooting, describes the best practices to tune, debug, and troubleshoot typical use cases.
Chapter 13, Data Analysis, describes common options to analyze data using AWS analytics services.
Chapter 14, Machine Learning Integration, describes how to utilize your data for a machine learning workload.
Chapter 15, Architecting Data Lakes for Real-World Scenarios and Edge Cases, describes end-to-end examples of architecting data lakes.
