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Data Engineering with AWS

Data Engineering with AWS

By : Eagar
4.7 (24)
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Data Engineering with AWS

Data Engineering with AWS

4.7 (24)
By: 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)
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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

Understanding the core concepts for pipeline orchestration

In Chapter 5, Architecting Data Engineering Pipelines, we architected a high-level overview of a data pipeline. We examined potential data sources, discussed the types of data transformations that may be required, and looked at how we could make transformed data available to our data consumers.

Then, we examined the topics of data ingestion, transformation, and how to load transformed data into data marts in more detail in the subsequent chapters. As we discussed previously, these steps are often referred to as an extract, transform, load (ETL) process.

We have now come to the part where we need to combine the individual steps involved in our ETL processes to operationalize and automate how we process data. But before we look deeper at the AWS services for enabling this, let's examine some of the key concepts around pipeline orchestration.

What is a data pipeline, and how do you orchestrate it?

A simple definition...

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