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

Exploring AWS services for ML

AWS has three broad categories of ML and AI services, as illustrated in the following diagram (note that only a small sample of AI and ML services are included in this diagram, due to space constraints):

Figure 13.1 – Amazon ML/AI stack

In the preceding diagram, we can see a subset of the services that AWS offers in each category – Artificial Intelligence Services, Machine Learning Services, and Machine Learning Frameworks and Infrastructure.

At the ML framework level, AWS provides Amazon Machine Images (AMIs) and prebuilt Docker containers that have popular deep learning ML frameworks pre-installed and optimized for the AWS environment. While these are useful for advanced use cases that require custom ML environments, these use cases are beyond the scope of this book.

For more information on these ML frameworks, refer to the AWS documentation on AWS Deep Learning AMIs (https://aws.amazon.com/machine-learning...