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• LDR

  00000cam c2200205 c 4500

• 001

  000005131784

• 005

  20240221185456

• 007

  ta

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  240220s2022 caua b 001c0 eng d

• 020

  ▼a  9781098108304
  ▼q  paperback

• 020

  ▼a  1098108302

• 040

  ▼a  211070
  ▼c  211070
  ▼d  211070

• 050

  ▼a  QA76.9.D26

• 090

  ▼a  QA76.9.D26
  ▼b  R375

• 245

  ▼a  Fundamentals of data engineering :
  ▼b  plan and build robust data systems /
  ▼d  Joe Reis ,
  ▼e  Matt Housley

• 250

  ▼a  First edition

• 260

  ▼a  Sebastopol, CA :
  ▼b  O'Reilly Media,
  ▼c  2022

• 300

  ▼a  xix, 422 pages :
  ▼b  illustrations (black and white) ;
  ▼c  24 cm

• 504

  ▼a  Includes bibliographical references and index.

• 505

  ▼a  Data engineering described -- The data engineering lifecycle -- Designing good data architecture -- Choosing technologies across the data engineering lifecycle -- Data generation in source systems -- Storage -- Ingestion -- Queries, modeling, and transformation -- Serving data for analytics, machine learning, and reverse ETL -- Security and privacy -- The future of data engineering.

• 520

  ▼a  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: Get a concise overview of the entire data engineering landscape ; 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.

• 650

  ▼a  Database design.

• 650

  ▼a  Computer architecture.

• 650

  ▼a  Database management.

• 650

  ▼a  Big data.

• 650

  ▼a  COMPUTERS / Data Science / Data Modeling & Design.
  ▼2  bisacsh

• 650

  ▼a  Database management.
  ▼2  fast
  ▼0  (OCoLC)fst00888037

• 650

  ▼a  Big data.
  ▼2  fast
  ▼0  (OCoLC)fst01892965

• 650

  ▼a  Computer architecture.
  ▼2  fast
  ▼0  (OCoLC)fst00872026

• 650

  ▼a  Database design.
  ▼2  fast
  ▼0  (OCoLC)fst00888032

• 700

  ▼a  Housley, Matthew L.,
  ▼d  1977-,
  ▼e  author.

• 950

  ▼b  ￡63.99

• 991

  ▼a  단행본