Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Data Engineering with AWS
  • Table Of Contents Toc
Data Engineering with AWS

Data Engineering with AWS

By : Gareth Eagar
4.7 (24)
close
close
Data Engineering with AWS

Data Engineering with AWS

4.7 (24)
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)
close
close
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...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Data Engineering with AWS
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon