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

Approaching the data pipeline architecture

Before we get into the details of the individual components that will go into the architecture, it is helpful to get a 10,000 ft view of what we're trying to do.

A common mistake when starting a new data engineering project is to try and do everything at once, and to create a solution that covers all use cases. A better approach is to identify an initial, specific use case, and to start the project while focusing on that one outcome, but keeping the bigger picture in mind.

This can be a significant challenge, and yet it is really important to get this balance right. While you need to focus on an achievable outcome that can be completed within a reasonable time frame, you also need to ensure that you're building within a framework that can be used for future projects. If each business unit tackles the challenge of data analytics independently, with no corporate-wide analytics initiative, it will be difficult to unlock the value...