Big Data Vendor Revenue And Market Forecast 2012-2017 - Wikibon
As mentioned in the introduction of this report, Hadoop-related software and services matured rapidly in 2012, leading to increased adoption of enterprise-level products by companies in industries beyond the Web. In many cases, companies that had previously deployed community (read: free) versions of vendor Big Data software bundles for proof-of-concept projects began upgrading to paid software and services to support production-level deployments.
As a result, leading Hadoop distribution vendors Cloudera and MapR enjoyed significant revenue growth last year. Cloudera grew revenue to $56 million in 2012 from $18 million in 2011. MapR grew revenue to $23 million in 2012 from $7 million in 2011. Hortonworks, in its first full year of existence, did $18 million in revenue in 2012.
Likewise, in the related NoSQL space a handful of vendors that offer commercial versions of popular open source databases enjoyed significant revenue growth as pilot projects blossomed into production deployments supporting real-time, Web-scale applications and services.
Among these vendors is 10gen, which offers a commercial version of the open source, document-oriented MongoDB; Aerospike, whose NoSQL database supports very low-latency online transactional applications; and DataStax, the company behind commercial Cassandra that counts Netflix among its marquee customers.
Leading the way in terms of revenue in the Hadoop/NoSQL subsegment of the Big Data market in 2012 was a 10-year-old firm, MarkLogic. The company's NoSQL document store is in use at Bank Of America, the Defense Intelligence Agency and Warner Brothers, among other household names in the media and financial services industries.
Ultimately, however, the NoSQL market is largely up for grabs. Each NoSQL database has its related strengths and weaknesses, and no one NoSQL database currently "does it all." Big Data practitioners must take a number of factors into consideration when selecting a NoSQL database to facilitate large-scale transactional workloads, including scalability, performance, security, and ease-of-development.
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