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DTSTAMP:20260905T134929Z
DESCRIPTION:Click for Latest Location Information: http://edw2023fall.datav
 ersity.net/sessionPop.cfm?confid=157&proposalid=14607\nSupporting analytics
  and data science in an enterprise involves more than installing open sourc
 e or using cloud services. Too often, the focus is on technology when it sh
 ould be on data when the goal is to build multi-purpose infrastructure. Thi
 s session discusses architecture principles, design assumptions, and the da
 ta architecture 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 archit
 ecture.\n\nArchitecture starts with uses and includes the data, technology,
  methods of building and maintaining, governance, and organization of peopl
 e. What are the design principles that lead to good design and data archite
 cture? What assumptions limit older approaches? How can one modernize an ex
 isting data environment? How will this affect data management? This se
 ssion will help you answer these questions.\n\nYou will learn:   
  \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\n
DTSTART:20230921T083000
SUMMARY:S3: Data Architecture to Modernize Analytics and Data Science
DTEND:20230921T155959
LOCATION: See Description
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