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

Meeting the needs of data scientists and ML models

Over the past decade, the field of ML has significantly expanded, and the majority of larger organizations now have data science teams that use ML techniques to help drive the objectives of the organization.

Data scientists use advanced mathematical concepts to develop ML models that can be used in various ways, including the following:

  • Identifying non-obvious patterns in data (based on the results of a blood test, what is the likelihood that this patient has a specific type of cancer?)
  • Predicting future outcomes based on historical data (is this consumer, with these specific attributes, likely to default on their debt?)
  • Extracting metadata from unstructured data (in this image of a person, are they smiling? Are they wearing sunglasses? Do they have a beard?)

Many types of ML approaches require large amounts of raw data to train the machine learning model (teaching the model about patterns in data). As such...