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DTSTAMP:20260305T101739Z
DESCRIPTION:Click for Latest Location Information: http://edw2024.dataversi
 ty.net/sessionPop.cfm?confid=159&proposalid=15028\nSupporting analytics and
  data science in an enterprise involves more than installing open source or
  using cloud services. Too often, the focus is on technology when it should
  be on data when the goal is to build multi-purpose infrastructure. This se
 ssion discusses architecture principles, design assumptions, and the data a
 rchitecture and data management needed to build good infrastructure.\n\nThe
  focus in our market has been on acquiring technology, and that ignores the
  more important part: the landscape within which this technology exists and
  the data architecture that lies at its core. If one expects longevity from
  a platform, then it should be a designed rather than accidental architectu
 re.\n\nArchitecture starts with uses and includes the data, technology, met
 hods of building and maintaining, governance, and organization of people. W
 hat are the design principles that lead to good design and data architectur
 e? What assumptions limit older approaches? How can one modernize an existi
 ng data environment? How will this affect data management? This sessio
 n will help you answer these questions.\n\nYou will learn:   &nbs
 p;\n\n	How data architecture influences your technology architecture\n
 Analytic workloads and their impact on data architecture and technology cho
 ices\n
 How to separate infrastructure from application in your data systems\n
 Tradeoffs and considerations in multi-application and multi-use data infras
 tructure\n
 When to save immutable data, when to standardize data, and how to govern it
 s delivery and use over time\n
DTSTART:20240328T083000
SUMMARY:W3: Data Architecture to Modernize Analytics and Data Science
DTEND:20240328T155959
LOCATION: See Description
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