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.