9781098108304-1098108302-Fundamentals of Data Engineering: Plan and Build Robust Data Systems

Fundamentals of Data Engineering: Plan and Build Robust Data Systems

ISBN-13: 9781098108304
ISBN-10: 1098108302
Edition: 1
Author: Joe Reis, Matt Housley
Publication date: 2022
Publisher: O'Reilly Media
Format: Paperback 447 pages
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Book details

ISBN-13: 9781098108304
ISBN-10: 1098108302
Edition: 1
Author: Joe Reis, Matt Housley
Publication date: 2022
Publisher: O'Reilly Media
Format: Paperback 447 pages

Summary

Fundamentals of Data Engineering: Plan and Build Robust Data Systems (ISBN-13: 9781098108304 and ISBN-10: 1098108302), written by authors Joe Reis, Matt Housley, was published by O'Reilly Media in 2022. With an overall rating of 3.9 stars, it's a notable title among other Data Modeling & Design (Databases & Big Data, Data Mining, Data Processing, Cloud Computing, Networking & Cloud Computing, Databases, Software) books. You can easily purchase or rent Fundamentals of Data Engineering: Plan and Build Robust Data Systems (Paperback) from BooksRun, along with many other new and used Data Modeling & Design books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $22.25.

Description

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you will learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available in the framework of the data engineering lifecycle.

Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You will understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, governance, and deployment that are critical in any data environment regardless of the underlying technology.

This book will help you:

  • Assess data engineering problems using an end-to-end data framework of best practices
  • Cut through marketing hype when choosing data technologies, architecture, and processes
  • Use the data engineering lifecycle to design and build a robust architecture
  • Incorporate data governance and security across the data engineering lifecycle

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