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Serverless ETL and Analytics with AWS Glue : Design scalable data lakes, optimize ETL pipelines, and accelerate analytics on AWS (2ND)

Sekiyama, Noritaka/ Quiroga, Albert/ Tanaka, Tomohiro
87.79 SGD
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79.02
English

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Product Description

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 DescriptionWhether 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 Opti
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Build scalable, cost-efficient data platforms with AWS Glue and automate complex ETL pipelines to power modern analytics and governed data lakes

Key Features

Master production-grade AWS Glue jobs on serverless Spark for enterprise-scale ETL
Design governed data lakes using Glue Data Catalog and Lake Formation
Written by AWS Glue community members, this practical guide shows you how to implement AWS Glue in no time

Book DescriptionBuilding a modern data platform is no longer just about moving data. Organizations must scale reliably, control costs, enforce governance, and accelerate analytics. This book shows you how to design and operate production-grade data platforms using AWS Glue and related AWS analytics services.
You will begin with core data management concepts before moving into ingestion from diverse sources, data preparation strategies, metadata management, security controls, and cross-account data sharing. Learn how to design efficient data layouts, orchestrate pipelines, implement CI CD practices, and manage the full lifecycle of data integration workloads.
This updated edition expands coverage of open table formats such as Apache Hudi, Delta Lake, and Apache Iceberg, along with performance tuning, observability, cost optimization, and real-world troubleshooting. You will also explore integrations with machine learning and generative AI workflows powered by Glue and SageMaker.
Written by AWS engineers and architects with deep hands-on experience in large-scale enterprise data lakes, this guide blends architecture principles with real-world implementation insight.
By the end of this book, you will be able to design, deploy, monitor, and optimize scalable serverless ETL pipelines and governed data platforms on AWS.What you will learn

Design scalable serverless ETL pipelines with AWS Glue
Ingest data from files, streams, SaaS, and JDBC sources
Optimize data layout and storage for faster analytics
Implement metadata governance and lineage tracking
Secure data with access control, encryption, and auditing
Automate CI CD for production data pipelines
Tune performance and troubleshoot Glue workloads
Apply open table formats for modern data lakes

Who this book is forThis book is for data engineers, ETL developers, cloud architects, and analytics professionals building scalable data platforms on AWS. If you are designing serverless data lakes, optimizing Spark-based ETL pipelines, or implementing governance and cost controls across analytics workloads, this guide is for you. A foundational understanding of AWS services and data processing concepts will help you get the most from this book.

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