Organizations these days have gravitated toward services such as AWS Glue that undertake undifferentiated heavy lifting and provide serverless Spark, enabling you to create and manage data lakes in a serverless fashion. This guide shows you how AWS Glue can be used to solve real-world problems along with helping you learn about data processing, data integration, and building data lakes.
Beginning with AWS Glue basics, this book teaches you how to perform various aspects of data analysis such as ad hoc queries, data visualization, and real-time analysis using this service. It also provides a walk-through of CI/CD for AWS Glue and how to shift left on quality using automated regression tests. You’ll find out how data security aspects such as access control, encryption, auditing, and networking are implemented, as well as getting to grips with useful techniques such as picking the right file format, compression, partitioning, and bucketing. As you advance, you’ll discover AWS Glue features such as crawlers, Lake Formation, governed tables, lineage, DataBrew, Glue Studio, and custom connectors. The concluding chapters help you to understand various performance tuning, troubleshooting, and monitoring options.
By the end of this AWS book, you’ll be able to create, manage, troubleshoot, and deploy ETL pipelines using AWS Glue.

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Spis treści

Serverless ETL and Analytics with AWS Glue. Your comprehensive reference guide to learning about AWS Glue and its features eBook -- spis treści

• 1. Data Management – Introduction and Concepts
• 2. Introduction to Important AWS Glue Features
• 3. Data Ingestion
• 4. Data Preparation
• 5. Designing Data Layouts
• 6. Data Management
• 7. Metadata Management
• 8. Data Security
• 9. Data Sharing
• 10. Data Pipeline Management
• 11. Monitoring
• 12. Tuning, Debugging, and Troubleshooting
• 13. Data Analysis
• 14. Machine Learning Integration
• 15. Architecting Data Lakes for Real-World Scenarios and Edge Cases