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

Designing a high-performance data warehouse

When you're looking to design a high-performing data warehouse, multiple factors need to be considered. These include items such as cluster type and sizing, compression types, distribution keys, sort keys, data types, and table constraints.

As part of the design process, you will need to consider several trade-offs, such as cost verse performance or the size of storage verse performance. Business requirements and the available budget will often drive these decisions.

Beyond decisions about infrastructure and storage, the logical schema design also plays a big part in optimizing the performance of the data warehouse. Often, this will be an iterative process, where you start with an initial schema design that you refine over time to optimize for increased performance.

Selecting the optimal Redshift node type

There are different types of nodes available, each with different combinations of CPU, memory, storage capacity, and...