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

Chapter 9: Loading Data into a Data Mart

While the data lake enables a significant amount of analytics to happen inside it, there are several use cases where a data engineer may need to load data into an external data warehouse, or data mart, to enable a set of data consumers.

As we reviewed in Chapter 2, Data Management Architectures for Analytics, a data lake is a single source of truth across multiple lines of business, while a data mart contains a subset of data of interest to a particular group of users. A data mart could be a relational database, a data warehouse, or a different kind of data store.

Data marts serve two primary purposes. First, they provide a database with a subset of the data in the data lake, optimized for specific types of queries (such as for a specific business function). In addition, they also provide a higher-performing, lower latency query engine, which is often required for specific analytic use cases (such as for powering business intelligence...