A brief rant here: I am asked with great frequency how this RDBMS will hold off that big data play, how data warehouses will survive in a world where Hadoop exists, or whether Apple is done now that Android is doing well. There is a fundamental fallacy implicit in these questions.
Comparing what someone new and shiny may be claiming they will do a year from now with what someone established is already doing today is foolish. The established vendor being compared is not likely to stand still. In fact, it may well have got where it is precisely because it has learned to sustain innovation. In the big data world, to acknowledge that, say, the uniqueness of MapR’s current storage solution compared to HDFS will likely erode over time is accurate. But to assume MapR will stand still while that happens is not; they are several releases, and several different innovations, in. They still may fall behind – but not because they stood still.
How do I handle these questions as an analyst? By sticking with what is shipping, in production, with referenceable customers. To advise someone who has a need for technology that they should wait until some uncertain point in time when an open source provider may have some technology ready that will compete with today’s enterprise-ready, supported product strikes me as very poor advice. If they don’t need it now, they should wait anyway, and evaluate the options when they do.
This ties closely to my often-offered comment that is it is the Silly Con Valley (thanks to Paul Kent at SAS for that one) disease to believe that once we write it on the whiteboard it’s ready. It’s bad enough to compare to what we know will go GA at a relatively predictable time (like a SQL Server release) but to compare to something whose feature list is on a request for volunteers at an open source meetup is entirely different.
Read Complimentary Relevant Research
Organizing for Big Data Through Better Process and Governance
With big data past the Peak of Inflated Expectations on the Hype Cycle, organizations are addressing next-level challenges and asking,...
View Relevant Webinars
Smart Machine Big-Bang Disruption Is Happening Now
At the intersection of advanced analytics, AI, extreme personalization, autonomous systems and big data, physical and virtual smart machines...
Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.