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When is a Data Lake an Enterprise Data Lake (EDL)?

By Andrew White | July 11, 2016 | 0 Comments

Information GovernanceData WarehouseData LakeDark DataData and Analytics Strategies

I was on one of those briefings where a ‘newish’, small vendor was telling me how unique they were and how their approach was brand new and never seen before.  The tenor of the call was typical of perhaps one or two such calls every 2-3 weeks.  There is a lot of innovation going on, “out there”, isn’t there…

However, in this case, the vendor seemed to be arguing for a concept for what I might call an enterprise data lake. Have you heard that one before?  It was as if the vendor message was along the lines of:

  • What everyone has tried with data lakes thus far has failed since they are all siloed.
  • All data will be unified in one solution (ours, of course!).
  • Our solution is cheaper, faster, and easier to use than anything else on the market, especially from those big vendor solutions.
  • This is a stack in which all data will (miraculously) be understandable (as in semantically modeled) and all such source data can then be eliminated.
  • We don’t copy data – there is only one version of it.  So duplication are removed.
  • This stack IS the data stack for the digital economy.  Operational and analytical data; content and data; all.
  • We tag data – all data.  This means we can govern data.

I think the vendor perceives that data lakes, which are not really that old, are already out of data.  They are (apparently) costly, slow and inefficient.  These guys have the next best thing – an enterprise data lake.  It seemed to me that this was a combination of every solution on the planet, wrapped up into a single platform.  Now that sounds different, doesn’t it?

They came to meet me since we were going to explore how their unique approach to governing data in their enterprise data lake was a silver bullet and better than anything else in history.  Could be a long time coming.

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