Use AWS Glue to integrate growing data sources with serverless ETL, building secure, observable pipelines that support reliable analytics while managing performance and cost across a governed AWS data platform as workloads grow Key Features: - Use runnable code, console walkthroughs, and downloadable examples for core AWS Glue workflows - Apply DataOps practices with AWS CDK, Docker, and CI/CD in real-world scenarios - Learn from six data specialists with AWS, Spark, Apache Iceberg, and data lake expertise Book Description: Whether you build data pipelines, design cloud architectures, or deliver analytics on AWS, bringing data together is only part of the challenge. You must also keep this data clean, trustworthy, and available while controlling costs. AWS Glue offers serverless data integration, but using it effectively requires decisions about storage, metadata, security, orchestration, monitoring, and performance. This book guides you from modern data management and core AWS Glue features through ingestion from files, streams, SaaS applications, and JDBC sources, preparation, storage layout, metadata, security, sharing, and pipeline operations. Console walkthroughs and runnable examples show how to manage schemas and lineage in AWS Glue Data Catalog, apply AWS Lake Formation access controls, monitor workloads, tune Spark jobs, troubleshoot failures, and manage development with AWS CDK, Docker, and CI/CD. You will also examine analytics, machine learning and generative AI integrations, real-world data lake scenarios, and cost optimization. Learn how Apache Iceberg, Apache Hudi, and Delta Lake add transactions, schema evolution, and efficient data management to data lakes. By the end, you will be able to design, build, operate, and continuously improve a serverless data platform that fits your organization's scale, structure, and priorities. What You Will Learn: - Design scalable serverless ETL pipelines with AWS Glue - Ingest data from files, streams, SaaS, and JDBC sources - Optimize file formats, partitions, compression, and layouts - Manage schemas, partitions, and lineage in AWS Glue Data Catalog - Secure data with access control, encryption, and auditing - Automate testing and multi account CI/CD using AWS CDK and Docker - Monitor, tune, and troubleshoot AWS Glue and Spark workloads - Apply Apache Iceberg, Hudi, and Delta Lake to data lakes with AWS Glue Who this book is for: This book is for data engineers, ETL developers, cloud architects, and analytics professionals who build or operate data platforms on AWS. It suits readers working on serverless data lakes, Spark ETL, governance, data sharing, reliability, or cost control. It is especially useful if you aim to improve pipeline reliability, governance, or cost visibility as workloads grow. Basic familiarity with the AWS Management Console, Amazon S3, and IAM is recommended. Experience with Python, SQL, or Apache Spark will help with the code examples, and an AWS account is useful for following the walkthroughs. Table of Contents - Data Management: Introduction and Concepts - Introduction to Important AWS Glue Features - Data Ingestion - Data Preparation - Data Layouts - Data Management - Implementing Resilient Metadata Management - Data Security - Data Sharing - Data Pipeline Management - Monitoring - Tuning, Debugging and Troubleshooting - Data Analysis - Machine Learning and Generative AI Integration - Architecting Data Lakes for Real World Scenarious and Edge Cases - End-to-end development lifecycle - Open Table Format - Cost Optimization