Data Engineering with AWS: Acquire the skills to design and build AWS-based data transformation pipelines like a pro 2nd Edition

Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered.

Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

• Delve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines
• Stay up to date with a comprehensive revised chapter on Data Governance
• Build modern data platforms with a new section covering transactional data lakes and data mesh

Book Description

This book, authored by a Senior Data Architect with 25 years of experience, helps you gain expertise in the AWS ecosystem for data engineering. This revised edition updates every chapter to cover the latest AWS services and features, provides a refreshed view on data governance, and introduces a new section on building modern data platforms. You will learn how to implement a data mesh, work with open-table formats such as Apache Iceberg, and apply DataOps practices for automation and observability.

You will begin by exploring core concepts and essential AWS tools used by data engineers, along with modern data management approaches. You will then design and build data pipelines, review raw data sources, transform data, and understand how it is consumed by various stakeholders. The book also covers data governance, populating data marts and warehouses, and how a data lakehouse fits into the architecture. You will explore AWS tools for analysis, SQL queries, visualizations, and learn how AI and machine learning generate insights from data. Later chapters cover transactional data lakes, data meshes, and building a complete AWS data platform.

By the end, you will be able to confidently implement data engineering pipelines on AWS.

*Email sign-up and proof of purchase required

What you will learn

• Seamlessly ingest streaming data with Amazon Kinesis Data Firehose
• Optimize, denormalize, and join datasets with AWS Glue Studio
• Use Amazon S3 events to trigger a Lambda process to transform a file
• Load data into a Redshift data warehouse and run queries with ease
• Visualize and explore data using Amazon QuickSight
• Extract sentiment data from a dataset using Amazon Comprehend
• Build transactional data lakes using Apache Iceberg with Amazon Athena
• Learn how a data mesh approach can be implemented on AWS

Who this book is for

This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.

Table of Contents

1. An Introduction to Data Engineering
2. Data Management Architectures for Analytics
3. The AWS Data Engineer's Toolkit
4. Data Governance, Security, and Cataloging
5. Architecting Data Engineering Pipelines
6. Ingesting Batch and Streaming Data
7. Transforming Data to Optimize for Analytics
8. Identifying and Enabling Data Consumers
9. A Deeper Dive into Data Marts and Amazon Redshift
10. Orchestrating the Data Pipeline
11. Ad Hoc Queries with Amazon Athena
12. Visualizing Data with Amazon QuickSight

(N.B. Please use the Read Sample option to see further chapters)