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Clean Data at Scale with AWS Glue DataBrew: Practical Workflows for Cleaning, Enriching, and Automating Data Pipelines
Clean Data at Scale with AWS Glue Practical Workflows for Cleaning, Enriching, and Automating Data Pipelines is a hands-on guide that shows you how to streamline data preparation across the enterprise. Combining clear explanations of DataBrew’s capabilities with practical examples, the book walks through data acquisition, interactive and programmatic transformation, automated recipe design, and data profiling so teams can quickly identify and resolve quality issues. You’ll learn how DataBrew fits into the broader AWS analytics ecosystem, with patterns for integrating Glue, S3, Redshift, Athena, and SageMaker to build end-to-end pipelines.
The book dives into advanced topics that matter at orchestrating multi-service workflows, optimizing performance and cost for massive datasets, securing data in transit and at rest, and implementing governance, lineage, and compliance controls. Chapters cover enrichment and feature engineering for ML, monitoring and alerting best practices, robust error handling, and CI/CD approaches for recipes and jobs. Real-world case studies illustrate how to design repeatable, maintainable workflows that reduce manual effort and accelerate time to insight.
Written for data engineers, analytics leaders, and ML practitioners, this practical reference blends architectural guidance, hands-on recipes, and operational playbooks to help teams deliver trusted data faster. Whether you are building repeatable ETL processes, preparing features for models, or enforcing regulatory requirements, Clean Data at Scale with AWS Glue DataBrew equips you with the strategies and patterns to build secure, cost-effective, and governed data pipelines that scale with your business.
The book dives into advanced topics that matter at orchestrating multi-service workflows, optimizing performance and cost for massive datasets, securing data in transit and at rest, and implementing governance, lineage, and compliance controls. Chapters cover enrichment and feature engineering for ML, monitoring and alerting best practices, robust error handling, and CI/CD approaches for recipes and jobs. Real-world case studies illustrate how to design repeatable, maintainable workflows that reduce manual effort and accelerate time to insight.
Written for data engineers, analytics leaders, and ML practitioners, this practical reference blends architectural guidance, hands-on recipes, and operational playbooks to help teams deliver trusted data faster. Whether you are building repeatable ETL processes, preparing features for models, or enforcing regulatory requirements, Clean Data at Scale with AWS Glue DataBrew equips you with the strategies and patterns to build secure, cost-effective, and governed data pipelines that scale with your business.
334 pages, Kindle Edition
Published October 9, 2025
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