On Demand Webinar

Data Warehouse + Data Lake: Get the Synergy, Ditch the Complexity

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The question of whether data lakes or data warehouses provide more efficacy is now almost cliché.  In the classic version of the story, business users suffer collateral damage as these two conflicting models duke it out, and one or the other wins.  In the alternate ending, the two models are unified, each leveraged for its relative strength, leaving business users to benefit greatly.

Both of these endings involve a little wishful thinking, frankly.  Can business users really do without the flexibility and agility of a data lake, or can they dispense with the rigor and governance of a data warehouse?  And, conversely, can they really have it both ways, without a monumental data modeling and ETL burden?

When at an impasse in data management, applying data-driven thinking can often help. What if a virtual data layer could be formed over warehouse and lake, and what if the ETL and modeling burden to build that layer were automated, through the use of machine learning?  Now the warehouse and lake can exist separately, unified virtually, and interfaced autonomously.

Can this really be done?  Can performance be acceptable?  Join us for this free 1-hour webinar from GigaOm Research.  The webinar features GigaOm analyst Andrew Brust and special guest, Roi Avinoam, Co-founder and CTO from Panoply, a company specializing in smart data warehouse technology.

In this 1-hour webinar, you will discover:

  • How warehouse and lake can be unified virtually while developing separately
  • How and when data in the lake can “graduate” to become part of the warehouse.
  • How existing database technologies like views and caching can be applied in new ways to address the new requirements of data-driven organizations

Register now to join GigaOm Research and Panoply for this free expert webinar.

Who Should Attend:

  • CTOs
  • Chief Data Officers
  • Business Intelligence Architects
  • Business Analysts
  • Data Engineers
  • Database developers

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