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

Bringing together the best of both worlds with the lake house architecture

In today's highly digitized world, data about customers, products, operations, and the supply chain can come from many sources and can have a diverse set of structures. To gain deeper and more complete data-driven insights about a business topic (such as the customer journey, customer retention, product performance, and more), organizations need to analyze all the relevant topic data of all the structures from all the sources, together.

Organizations collect and analyze structured data in data warehouses, and they build data lakes to manage and analyze unstructured data. Historically, organizations have built data warehouse and data lake solutions in isolation from each other, with each having its own separate data ingestion, storage, processing, and governance layers. Often, these disjointed efforts to build separate data warehouse and data lake ecosystems have ended up creating data and processing...