Simplifying Access to Big Data
By Peter Jeffcock-Oracle on Jun 16, 2014
When we present on big data at events, talk about it on trade show stands or answer questions on webcasts, there’s a common refrain: what skills do I need for big data?
A year or two ago, the answer was often some flavor of “learn about Hadoop and MapReduce”. But things change. Today you might hear Hive, Impala, Stinger, Hadapt, Polybase, Shark, Lingual, Apache Phoenix, CitusDB, Presto or Drill in the answer. (There are others, but that’s enough to make the point). Apart from the creative naming for which the Hadoop ecosystem is well-known, these projects all have one thing in common: SQL.
SQL is the number one language for data management professionals and has been for a long time. As O’Reilly showed, it’s even preferred by data scientists over choices like R, Python or Excel. Vendors have figured that out and SQL implementations have been sprouting like weeds.
But which one should you pick? How complete is the implementation? How fast? How secure? And how do you use it to query data that’s not in Hadoop, like the critical stuff that’s in your data warehouse?
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