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Big Data is quickly going mainstream, but much of the buzz has focused on the various engines from Apache Hadoop to Apache Spark with less focus on the key business considerations that must be taken into account. The many use cases for big data make it easy to be taken in by all of the hype, but organizations run a huge risk if big data projects are unsuccessful or even never reach full production.
Big Data is quickly going mainstream, but much of the buzz has focused on the various engines from Apache Hadoop to Apache Spark with less focus on the key business considerations that must be taken into account. The many use cases for big data make it easy to be taken in by all of the hype, but organizations run a huge risk if big data projects are unsuccessful or even never reach full production. Currently only 13 percent of organizations acheive full-scale production for their in-house big data implementations, and only 27 percent of executives described their initiatives as successful.
Such low levels of success, should be telling for those organizations considering adopting big data to improve or run their business. The Hadoop ecosystem is complex, and failing to take that complexity into account when considering long-term performance can slow a project down tremendously. The infographic below identifies 5 key considerations when selecting either an on-premise or cloud-service vendor for a big data deployment.
