You're reading from Data Engineering with AWS - Second Edition

Product type Book

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Pages 636 pages

Edition 2nd Edition

Languages

Concepts

Data Engineering

Author (1):

Gareth Eagar

Table of Contents (24) Chapters

Preface

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

Identifying and Enabling Data Consumers

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

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Index

Hands-on – joining datasets with AWS Glue Studio

For our hands-on exercise in this chapter, we are going to use AWS Glue Studio to create an Apache Spark job that joins streaming data with data we migrated from our MySQL database in the previous chapter.

Creating a new data lake zone – the curated zone

As discussed in Chapter 2, Data Management Architecture for Analytics, it is common to have multiple zones in a data lake, containing different copies of our data as it gets transformed. So far, we have ingested raw data into the landing zone and then converted some of those datasets into Parquet format, and written the files out in the clean zone. In this chapter, we will be joining multiple datasets together and will write out the new dataset to the curated zone of our data lake. The curated zone is intended to store data that has been transformed and is ready for consumption by data consumers. We created an Amazon S3 bucket for the curated zone in a previous...