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

Chapter 13: Enabling Artificial Intelligence and Machine Learning

For a long time, organizations could only dream of the competitive advantage they would get if they could accurately forecast demand for their products, personalize recommendations for their customers, and automate complex tasks.

And yet, advancements in machine learning (ML) over the past decade or so have made many of these things, and much more, a reality.

ML describes the process of training computers in a way that mimics how humans learn to perform several tasks. ML uses a variety of advanced algorithms and, in most cases, large amounts of data to develop and train an ML model. This model can then be used to examine new data and automatically draw insights from that data.

ML offers a wide range of interesting use cases that are expected to have a growing impact on many different aspects of life. For example, scientists are using ML to analyze a patient's retina scan to identify early signs of Alzheimer...