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

Amazon Athena – in-place SQL analytics for the data lake

Structured Query Language (SQL) was invented at IBM in the 1970s but has remained an extremely popular language for querying data throughout the decades. Every day, millions of people across the world use SQL directly to explore data in a variety of databases, and many more use applications (whether business applications, mobile applications, or others) that, under the covers, use SQL to query a database.

Over the years, the American National Standards Institute (ANSI) has created various versions of an ANSI-SQL standard that database vendors can use to build ANSI-SQL-compliant databases. Database vendors often declare that their database is compatible with a large subset of ANSI-SQL, meaning that different database engines support different aspects of the ANSI-SQL standard.

Facebook, the social media network, has very large datasets and complex data analysis requirements and found that existing tools in the Hadoop...