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

Hands-on – loading data into an Amazon Redshift cluster and running queries

In our Redshift hands-on exercise, we're going to create a new Redshift cluster and set up Redshift Spectrum so that we can query data in external tables on Amazon S3. We'll then use Redshift Spectrum to read data from S3 and load a subset of that data into a local table in Redshift, after which we'll run some complex queries.

In this exercise, we will be setting up a Redshift cluster for a travel agency. Agents need to ensure that they can find the best deal for accommodation in New York City and Jersey City that is close to specific popular tourist attractions, such as the Freedom Tower and the Empire State Building.

Uploading our sample data to Amazon S3

For this exercise, we will use a dataset from an organization called Inside Airbnb (http://insideairbnb.com/about.html) that provides Airbnb data under the Creative Commons Attribution 4.0 International License (https://creativecommons...