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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
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13
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

Section 2: Architecting and Implementing Data Lakes and Data Lake Houses

In this section of the book, we examine an approach for architecting a high-level data pipeline and then dive into the specifics of data ingestion and transformation. We also examine different types of data consumers, learn about the important role of data marts and data warehouses, and finally put it all together by orchestrating data pipelines. We get hands-on with various AWS services for data ingestion (Amazon Kinesis and DMS), transformation (AWS Glue Studio), consumption (AWS Glue DataBrew), and pipeline orchestration (Step Functions).

This section comprises the following chapters:

  • Chapter 5, Architecting Data Engineering Pipelines
  • Chapter 6, Ingesting Batch and Streaming Data
  • Chapter 7, Transforming Data to Optimize for Analytics
  • Chapter 8, Identifying and Enabling Data Consumers
  • Chapter 9, Loading Data into a Data Mart
  • Chapter 10, Orchestrating the Data Pipeline
...
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