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Packt Publishing
Noritaka Sekiyama, Albert Quiroga, Tomohiro Tanaka ...
Serverless ETL And Analytics With AWS Glue
2nd Edition
2026
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Publisher > Packt Publishing
Author > Noritaka Sekiyama, Albert Quiroga, Tomohiro Tanaka ...
Title > Serverless ETL And Analytics With AWS Glue
Issue > 2nd Edition
Year > 2026
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ISBN > 9781835464847
Pages > 532
Genre > Etl Aws-Glue
Language > English
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Type > RETAIL
Media > TECH
Format > .epub
Section > eBook
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Published > 2026-08
Release > 2026-09-09
Disks > 04 x 5 MB
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- - -- [ D E S C R I P T I O N ] -- - -
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.
- - -- [ R E T A I L U R L ] -- - -
https://www.packtpub.com/en-us/product/serverless-etl-and-analytics-with-
aws-glue-9781835468012
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