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Data Engineering with AWS - Second Edition

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
Author (1):
Gareth Eagar Gareth Eagar
Profile icon 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 Other Books You May Enjoy
Index

Goals of a modern data platform

In Chapter 15, Implementing a Data Mesh Strategy, we discussed how a central data platform team is responsible for building a platform that makes it easy for both data producers and data consumers to work with organizational data.

A data platform is intended to provide a system where multiple teams from across an organization can easily ingest data (including both structured and semi-structured, via batch and streaming), process the ingested data, and create new data products by joining datasets. It should also provide data governance controls, a catalog for making data discoverable across the organization, and the ability to easily share datasets across different teams/data domains.

Let’s review some of the top goals for a modern data platform, after which we will explore approaches to building these data platforms.

A flexible and agile platform

As we all know, the only constant is change. We have seen this throughout this...

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