{"id":5738,"date":"2015-10-15T07:00:47","date_gmt":"2015-10-15T15:00:47","guid":{"rendered":"http:\/\/formtek.com\/blog\/?p=5738"},"modified":"2015-09-02T06:25:29","modified_gmt":"2015-09-02T14:25:29","slug":"big-data-challenges-facing-hadoop-in-the-enterprise","status":"publish","type":"post","link":"https:\/\/formtek.com\/blog\/big-data-challenges-facing-hadoop-in-the-enterprise\/","title":{"rendered":"Big Data: Challenges Facing Hadoop in the Enterprise"},"content":{"rendered":"

Hadoop has become the de facto tool for managing Big Data. \u00a0Hadoop provides a way to organize, search, share and analyze data that was collected from many different data sources. \u00a0And scalability from a few to dozens to thousands of servers is one of Hadoop’s most attractive features.<\/p>\n

But will enterprises be able to fully take advantage of the\u00a0benefits of Big Data analytics that Hadoop offers?<\/p>\n

David Richards<\/a>, CEO at WANdisco, took on that question and\u00a0said:\u00a0<\/a>“imagine a world in which one bank has Hadoop and everyone else is using proprietary storage \u2013 it is not going to work. \u00a0One bank will have a massive competitive advantage because they can scale up storage at ten percent the cost of everybody else. \u00a0The market for big data has got nothing to do with anything other than this: the requirement for data storage is growing at a sixty percent compound annual growth rate and budgets are not. So you can\u2019t use proprietary storage to solve that problem. \u00a0It isn\u2019t a technologically-driven market place; it is an economically-driven market place.”<\/p>\n

The problem is that Hadoop isn’t easy — at least not yet, although it is getting easier. \u00a0Enterprises are finding that taking advantage of Hadoop is challenging. \u00a0Tony Baer<\/a>, principal analyst at Ovum, identified two reasons<\/a> for this:<\/p>\n

Distributed Architecture.<\/strong> \u00a0“For one thing, it\u2019s a distributed architecture, not a database. \u00a0For anyone with database experience, distributed architectures have always been very difficult hurdle to deal with.\u201d \u00a0Hadoop’s distributed architecture is different from traditional enterprise software, and it doesn’t come prepackaged with tools like self-tuning, security and lifecycle management.<\/p>\n

Complexity.<\/strong> \u00a0A second factor is that it is a collection of projects rather than a single monolithic piece of software, and choosing the right pieces and learning them can be complex. \u00a0And more recently, with the introduction of tools like Spark, it is enabling\u00a0real-time analytics.\u00a0\u00a0\u201cIt [real-time capabilities] multiplies its value to organizations, but also multiplies the potential complexities you have to deal with.\u201d<\/p>\n

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Hadoop has become the de facto tool for managing Big Data. \u00a0Hadoop provides a way to organize, search, share and analyze data that was collected from many different data sources. \u00a0And scalability from a few to dozens to thousands of…<\/span><\/p>\n

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