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  <titleInfo>
    <title>Fundamentals of data engineering</title>
    <subTitle>plan and build robust data systems</subTitle>
  </titleInfo>
  <name type="personal">
    <namePart>Reis, Joe (Joseph)</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
    <role>
      <roleTerm type="text">author.</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Housley, Matthew L.</namePart>
    <namePart type="date">1977-</namePart>
    <role>
      <roleTerm type="text">author.</roleTerm>
    </role>
  </name>
  <typeOfResource>text</typeOfResource>
  <genre authority="marc">bibliography</genre>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">cau</placeTerm>
    </place>
    <dateIssued encoding="marc">2022</dateIssued>
    <edition>First edition.</edition>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">print</form>
    <extent>xix, 422 pages : illustrations (black and white) ; 24 cm</extent>
  </physicalDescription>
  <abstract>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.</abstract>
  <note type="statement of responsibility">Joe Reis and Matt Housley.</note>
  <note>Includes bibliographical references and index.</note>
  <subject authority="lcsh">
    <topic>Database design</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Computer architecture</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Database management</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Big data</topic>
  </subject>
  <subject authority="bisacsh">
    <topic>COMPUTERS / Data Science / Data Modeling &amp; Design</topic>
  </subject>
  <subject authority="fast">
    <topic>Database management</topic>
  </subject>
  <subject authority="fast">
    <topic>Big data</topic>
  </subject>
  <subject authority="fast">
    <topic>Computer architecture</topic>
  </subject>
  <subject authority="fast">
    <topic>Database design</topic>
  </subject>
  <classification authority="ddc" edition="23">005.743 REF 2022</classification>
  <identifier type="isbn">9789355421548</identifier>
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    <recordCreationDate encoding="marc">260222</recordCreationDate>
    <recordChangeDate encoding="iso8601">20260222094713.0</recordChangeDate>
    <recordIdentifier source="BD-RjUL">86185</recordIdentifier>
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