Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS

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Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS

Author : Gareth Eagar

Publisher ‏ : Packt Publishing (December 29, 2021)

Language ‏ : ‎ English

Paperback ‏ : ‎ 482 Pages

1) Book Description

Key Features

Learn about common data architectures and modern approaches to generating value from big data

Explore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines

Learn how to architect and implement data lakes and data lakehouses for big data analytics from a data lakes expert

Book Description

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.

What you will learn

Understand data engineering concepts and emerging technologies

Ingest streaming data with Amazon Kinesis Data Firehose

Optimize, denormalize, and join datasets with AWS Glue Studio

Use Amazon S3 events to trigger a Lambda process to transform a file

Run complex SQL queries on data lake data using Amazon Athena

Load data into a Redshift data warehouse and run queries

Create a visualization of your data using Amazon QuickSight

Extract sentiment data from a dataset using Amazon Comprehend

Who this book is for

This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts while gaining practical experience with common data engineering services on AWS will also find this book useful.

A basic understanding of big data-related topics and Python coding will help you get the most out of this book but it's not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.

Table of Contents

An Introduction to Data Engineering

Data Management Architectures for Analytics

The AWS Data Engineer's Toolkit

Data Cataloging, Security and Governance

Architecting Data Engineering Pipelines

Ingesting Batch and Streaming Data

Transforming Data to Optimize for Analytics

Identifying and Enabling Data Consumers

Loading Data into a Data Mart

Orchestrating the Data Pipeline

Ad Hoc Queries with Amazon Athena

Visualizing Data with Amazon QuickSight

Enabling Artificial Intelligence and Machine Learning

Wrapping Up the First Part of Your Learning Journey

2) About Author

Gareth Eagar has worked in the IT industry for over 25 years, starting in South Africa, then working in the United Kingdom, and now based in the United States. In 2017, he started working at Amazon Web Services (AWS) as a solution architect, working with enterprise customers in the NYC metro area. Gareth has become a recognized subject matter expert for building data lakes on AWS, and in 2019 he launched the Data Lake Day educational event at the AWS Lofts in NYC and San Francisco. He has also delivered a number of public talks and webinars on topics relating to big data, and in 2020 Gareth transitioned to the AWS Professional Services organization as a senior data architect, helping customers architect and build complex data pipelines.