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DTSTAMP:20260415T103505Z
DESCRIPTION:Click for Latest Location Information: http://das2018.dataversi
 ty.net/sessionPop.cfm?confid=124&proposalid=10041\n<p>What&rsquo;s happened
  with data organization?</p>\n<p>The rise of &ldquo;big data&rdquo; gave ri
 se to flat data lake architectures.&nbsp;Flat data lake architectures are g
 enerally preferred for machine learning applications.&nbsp;But, facing ever
 -growing data volumes, many organizations are still struggling with answeri
 ng basic questions for simply running the business:</p>\n<ul style="list-st
 yle-type:circle;">\n	How many customers did I add/lose last week?\n
 Did we meet our revenue forecasts last quarter?\n\n<p>These fundamental que
 stions are usually better suited for traditional Business Intelligence tool
 s.&nbsp;But can traditional BI tools and AI/machine learning tools use the 
 same data structures efficiently?</p>\n<p>Yes, with thoughtful data archite
 cture, it&rsquo;s possible.</p>\nIn this talk, we will explore data archite
 ctures that make sense for both data scientists and business intelligence a
 nalysts. There&rsquo;s no need to replicate data to serve two functions; on
 e model can serve many.
DTSTART:20181011T140000
SUMMARY:Old Meets New with the All-Purpose Data Lake
DTEND:20181011T145959
LOCATION: See Description
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