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

Loading data into data marts

Many tools can work directly with data in the data lake, as we covered in Chapter 3, The AWS Data Engineer’s Toolkit. These include tools for ad hoc SQL queries (Amazon Athena), data processing tools (such as Amazon EMR and AWS Glue), and even specialized machine learning tools (such as Amazon SageMaker).

These tools read data directly from Amazon S3, but there are times when a use case may require much lower latency and higher performance reads of the data. Alternatively, there may be times when the use of highly structured schemas may best meet the analytic requirements of the use case. In these cases, loading data from the data lake into a data mart makes sense.

In analytic environments, a data mart is most often a data warehouse system (such as Amazon Redshift or Snowflake), but it could also be a relational database system (such as Amazon RDS for MySQL), depending on the use case’s requirements. In either case, the system will...