Trends For Big Data in 2016

The trend of more people who do more with their data at speed will keep on because 2016 will be the year that best practice gets clear.

The spread of self-service data analytics, together with widespread adoption of the cloud and Hadoop, are resulting in lots of changes that are creating change as well as excitement in the industry.

  1. The NoSQL takeover

NoSQL technologies, often associated with unstructured data, have seen significant adoption over the last twelve months. Going forward, the shift to NoSQL databases as a leading piece of the enterprise IT landscape gets clear as the benefits of schema-less database concepts become more pronounced.

Nothing shows the picture more clearly than looking at Gartner’s Magic Quadrant for Operational Database Management Systems, which in the past was dominated by Oracle, IBM, Microsoft and SAP.

  1. Apache Spark lights up big data.

Apache Spark has turned from a being a component of the Hadoop ecosystem into the big data platform of choice for a number of enterprises.

Spark offers significantly increased data processing speed in comparison with Hadoop, and is now the biggest big data open source project, according to Spark originator and Databricks co-founder, Matei Zaharia.

What is more, more and more compelling enterprise utilize cases around Spark are emerging. For instance, at Goldman Sachs, Spark has become the lingua franca of big data analytics.

  1. Hadoop projects mature: enterprises keep on their move from Hadoop proof of concepts to production.

According to a recent survey of 2200 Hadoop clients, only three percent of respondents anticipated that they will be doing less with Hadoop in the next twelve months and 76 percent of those who already utilized Hadoop which is planned on doing more in the next three months.

  1. Big data grows up: Hadoop adds to enterprise standards.

As further evidence to the developing trend of Hadoop becoming a main part of the enterprise IT landscape, investment will grow in the components that surround enterprise systems like security.

In addition, Apache Sentry project offers a system for enforcing fine-grained, role-base authorization to data as well as metadata that is stored on a Hadoop cluster.

These are the kinds of capabilities which customers expect from their enterprise-grade RDBMS platforms and are now getting to the forefront of the emerging big data technologies, as a result eliminating one more barrier to enterprise adoption.

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