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Data Engineering with AWS

Data Engineering with AWS - Second Edition

By : Gareth Eagar
4.8 (31)
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Data Engineering with AWS

Data Engineering with AWS

4.8 (31)
By: Gareth Eagar

Overview of this book

This book, authored by a Senior Data Architect with 25 years of experience, helps you gain expertise in the AWS ecosystem for data engineering. This revised edition updates every chapter to cover the latest AWS services and features, provides a refreshed view on data governance, and introduces a new section on building modern data platforms. You will learn how to implement a data mesh, work with open-table formats such as Apache Iceberg, and apply DataOps practices for automation and observability. You will begin by exploring core concepts and essential AWS tools used by data engineers, along with modern data management approaches. You will then design and build data pipelines, review raw data sources, transform data, and understand how it is consumed by various stakeholders. The book also covers data governance, populating data marts and warehouses, and how a data lakehouse fits into the architecture. You will explore AWS tools for analysis, SQL queries, visualizations, and learn how AI and machine learning generate insights from data. Later chapters cover transactional data lakes, data meshes, and building a complete AWS data platform. By the end, you will be able to confidently implement data engineering pipelines on AWS. *Email sign-up and proof of purchase required
Table of Contents (24 chapters)
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1
Section 1: AWS Data Engineering Concepts and Trends
6
Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations
13
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning
17
Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World
22
Other Books You May Enjoy
23
Index

Summary

In this chapter, you learned more about the broad range of AWS ML and AI services that AWS provides, and had the opportunity to get hands-on with Amazon Comprehend, an AI service for extracting insights from written text.

We discussed how ML and AI services can apply to a broad range of use cases, both specialized (such as detecting cancer early) and general (business forecasting or personalization).

We examined different AWS services related to ML and AI. We looked at how different Amazon SageMaker capabilities can be used to prepare data for ML, build models, train and fine-tune models, and deploy and manage models. SageMaker makes building custom ML models much more accessible to developers without existing expertise in ML.

We then looked at a range of AWS AI services that provide prebuilt and trained models for common use cases. We looked at services for transcribing text from audio files (Amazon Transcribe), for extracting text from forms and handwritten...

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Data Engineering with AWS
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