Data engineering with AWS : acquire the skills to design and build AWS-based data transformation pipelines like a pro / Gareth Eagar.

Includes index.

Section 1: AWS data engineering concepts and trends -- An introduction to data engineering -- Data management architectures for analytics -- The AWS data engineer's toolkit -- Data governance, security, and cataloging -- Section 2: Architecting and implementing data engineering pipelines and transformations -- Architecting data engineering pipelines -- Ingesting batch and streaming data -- Transforming data to optimize for analytics -- A deeper dive into data marts and amazon redshift -- Orchestrating the data pipeline -- Section 3: The bigger picture: data analytics, data visualization, and machine learning -- Ad hoc queries with amazon Athena -- Visualizing data with amazon quicksight -- Enabling artificial intelligence and machine learning -- Section 4: Modern strategies: open table formats, data mesh, DataOps, and preparing for the real world -- Building transactional data lakes -- Implementing a data mesh strategy -- Building a modern data platform on AWS -- Wrapping up the first part of your learning journey.

"This book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability. You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS. By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!"