Big Data Tech Hadoop and Spark Get Slow Start in Enterprise
There are plenty of success stories about #Hadoop in the enterprise, but those may be the exception and not the rule. @Gartner Research VP @MervAdrian provides a reality check on deployment rates, successes, and failures of big data technologies in the enterprise. You may have seen quite a few success stories about enterprises implementing Hadoop and related technologies as part of companies’ big data analytics programs. You may wonder if you are behind the curve if you haven’t implemented this open source big data technology yet. Here’s some reassurance for you — while there may be plenty of success stories to tell, they are the exception. Many projects don’t even get started, and many of the projects that do get started will fail. Gartner is forecasting that through 2018, 70% of Hadoop deployments will fail to meet cost savings and revenue generation objectives due to skills and integration challenges. It’s a prediction the company actually made back in 2016, and according to Gartner Research VP Merv Adrian, it’s a prediction that’s held up pretty well. Adrian provided an update for enterprise organizations — Hadoop and Spark: Understanding Open Source Opportunities and Risks — at the Gartner Data and Analytics Summit in Grapevine, Texas this month.
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