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DESCRIPTION:Click for Latest Location Information: http://das2018.dataversi
 ty.net/sessionPop.cfm?confid=124&proposalid=9962\n<p>A deliberate architect
 ure for data integration helps keep projects on schedule and on budget; imp
 roves data quality and data governance; and increases business users&rsquo;
  confidence in data. But many companies resort to a trial-and-error method 
 of gathering data that is disorganized and incomplete. In this workshop, yo
 u&rsquo;ll learn how to design a hybrid integration architecture that suppo
 rts data, application, and cloud integration. Data may be stored in relatio
 nal, columnar, or in-memory DBMS, NoSQL, or Hadoop; logical DW, data lake, 
 or data hub; data virtualization; data services, both inter- and intra-ente
 rprise; and, within analytical processes (data blending or preparation). We
  will review and contrast integration use cases and best practices spanning
  technologies. We will review roles&nbsp;and responsibilities of IT, data s
 cience teams, &ldquo;citizen&rdquo; data scientists, and business &ldquo;po
 wer&rdquo; users.</p>\n<p>Key topics:</p>\n\n
 Underlying concepts and architectures\n
 Use cases and best fit technologies\n	Best and pragmatic practices\n
 Roles and responsibilities of integrators\n\n<p><span style="font-size: 13p
 x;">Many enterprises have siloed integration efforts that are redundant and
  overlapping. This tutorial will discuss, contrast, and match various types
  of integration use cases, architectures, technologies, and products.</span
 ></p>\n
DTSTART:20181009T133000
SUMMARY:Designing a Deliberate Architecture for Data Engineering, Data Inte
 gration, Application Integration, and Data Preparation
DTEND:20181009T164459
LOCATION: See Description
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