Book Image

Data Engineering with AWS - Second Edition

By : Gareth Eagar
5 (1)
Book Image

Data Engineering with AWS - Second Edition

5 (1)
By: Gareth Eagar

Overview of this book

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!
Table of Contents (24 chapters)
1
Section 1: AWS Data Engineering Concepts and Trends
6
Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations
13
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning
17
Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World
22
Other Books You May Enjoy
23
Index

Approaching the data pipeline architecture

Before we get into the details of the individual components that will go into the architecture, it is helpful to get a 10,000 ft view of what we’re trying to do.

A common mistake when starting a new data engineering project is to try and do everything at once, creating a solution that covers all use cases. A better approach is to identify an initial, specific use case and start the project while focusing on that one outcome, but keeping the bigger picture in mind.

This can be a significant challenge, and yet it is really important to get this balance right. While you need to focus on an achievable outcome that can be completed within a reasonable time frame, you also need to ensure that you build within a framework that can be used for future projects. If each business unit tackles the challenge of data analytics independently, with no corporation-wide analytics initiative, it will be difficult to unlock the value of corporation...