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.
Pathak, Vishal: - Vishal Pathak is a Data Lab Solutions Architect at AWS. Vishal works with customers on their use cases, architects solutions to solve their business problems, and helps them build scalable prototypes. Prior to his journey in AWS, Vishal helped customers implement business intelligence, data warehouse, and data lake projects in the US and Australia.
Sekiyama, Noritaka: - Noritaka Sekiyama is a Senior Big Data Architect on the AWS Glue and AWS Lake Formation team. He has 11 years of experience working in the software industry. Based in Tokyo, Japan, he is responsible for implementing software artifacts, building libraries, troubleshooting complex issues and helping guide customer architectures
Vajiraya, Subramanya: - Subramanya Vajiraya is a Big data Cloud Engineer at AWS Sydney specializing in AWS Glue. He obtained his Bachelor of Engineering degree specializing in Information Science & Engineering from NMAM Institute of Technology, Nitte, KA, India (Visvesvaraya Technological University, Belgaum) in 2015 and obtained his Master of Information Technology degree specialized in Internetworking from the University of New South Wales, Sydney, Australia in 2017. He is passionate about helping customers solve challenging technical issues related to their ETL workload and implementing scalable data integration and analytics pipelines on AWS.
