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

Ingesting streaming data

An increasingly common source of data for analytic projects is data that is continually generated and needs to be ingested in near-real-time. Some common sources of this type of data are as follows:

  • Data from IoT devices (such as smartwatches, smart appliances, and so on)
  • Telemetry data from various types of vehicles (cars, airplanes, and so on)
  • Sensor data (from manufacturing machines, weather stations, and so on)
  • Live gameplay data from mobile games
  • Mentions of the company brand on various social media platforms

For example, Boeing, the aircraft manufacturer, has a system called Airplane Health Management (AHM) that collects in-flight airplane data and relays it in real time to Boeing systems. Boeing processes the information and makes it immediately available to airline maintenance staff via a web portal.

In this section, we will look at several tools and services for ingesting streaming data, as well as things to consider...