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English | 2022 | ISBN: 1800564988 | 435 pages | True PDF | 25.32 MB
Build efficient data lakes that can scale to virtually unlimited size using AWS Glue
Key Features
Learn to work with AWS Glue to overcome typical implementation challenges in data lakesCreate and manage serverless ETL pipelines that can scale to manage big dataWritten by AWS Glue community members, this practical guide shows you how to implement AWS Glue in no time
Book Description
Organizations these days have gravitated toward services such as AWS Glue that undertake undifferentiated heavy lifting and provide serverless Spark, enabling you to create and manage data lakes in a serverless fashion. This guide shows you how AWS Glue can be used to solve real-world problems along with helping you learn about data processing, data integration, and building data lakes.
Beginning with AWS Glue basics, this book teaches you how to perform various aspects of data analysis such as ad hoc queries, data visualization, and real-time analysis using this service. It also provides a walk-through of CI/CD for AWS Glue and how to shift left on quality using automated regression tests. You'll find out how data security aspects such as access control, encryption, auditing, and networking are implemented, as well as getting to grips with useful techniques such as picking the right file format, compression, partitioning, and bucketing. As you advance, you'll discover AWS Glue features such as crawlers, Lake Formation, governed tables, lineage, DataBrew, Glue Studio, and custom connectors. The concluding chapters help you to understand various performance tuning, troubleshooting, and monitoring options.
By the end of this AWS book, you'll be able to create, manage, troubleshoot, and deploy ETL pipelines using AWS Glue.
What you will learn
Apply various AWS Glue features to manage and create data lakesUse Glue DataBrew and Glue Studio for data preparationOptimize data layout in cloud storage to accelerate analytics workloadsManage metadata including database, table, and schema definitionsSecure your data during access control, encryption, auditing, and networkingMonitor AWS Glue jobs to detect delays and loss of dataIntegrate Spark ML and SageMaker with AWS Glue to create machine learning models
Who this book is for
This book is for ETL developers, data engineers, and data analysts who want to understand how AWS Glue can help you solve your business problems. Basic knowledge of AWS data services is assumed.
Table of Contents
Data Management – Introduction and ConceptsIntroduction to Important AWS Glue FeaturesData IngestionData PreparationDesigning Data LayoutsData ManagementMetadata ManagementData SecurityData SharingData Pipeline ManagementMonitoringTuning, Debugging, and TroubleshootingData AnalysisMachine Learning IntegrationArchitecting Data Lakes for Real-World Scenarios and Edge Cases
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