Book Image

Data Engineering with AWS

By : Gareth Eagar
Book Image

Data Engineering with AWS

By: Gareth Eagar

Overview of this book

Written by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.
Table of Contents (19 chapters)
1
Section 1: AWS Data Engineering Concepts and Trends
6
Section 2: Architecting and Implementing Data Lakes and Data Lake Houses
13
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

Understanding data warehouses and data marts – fountains of truth

An Enterprise Data Warehouse (EDW) is the central data repository that contains structured, curated, consistent, and trusted data assets that are organized into a well-modeled schema. The data assets in an EDW are made up of all the relevant information about key business domains and are built by integrating data sourced from the following places:

  • Run-the-business applications (ERPs, CRMs, Line of Business applications) that support all the key business domains across the enterprise.
  • External data sources such as data from partners and third parties.

An enterprise data warehouse provides business users and decision-makers with an easy-to-use, central platform that helps them find and analyze a well-modeled, well-integrated, single version of truth about various business subject areas such as customer, product, sales, marketing, supply chain, and more. Business users analyze data in the warehouse...