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How the Open Data Platform is Changing the Big Data Landscape

· Big Data Zone

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Last month, Pivotal, IBM, and Hortonworks made waves with the announcement of the Open Data Platform (ODP)—an attempt to standardize Hadoop and Greenplum. Now a month after the announcement, let's examine the reasoning for this move, and see if we can measure its successes or failures.

The stated goal of the ODP is to “accelerate the delivery of Big Data solutions by providing a well-defined core platform that enables its users to avoid vendor lock-in” (source). A representative from Pivotal stated that the goal of the organization was “Linux-like,” so that users of Hadoop distributions could switch from one distribution to the other with the confidence that the core kernal was identical.

One big success so far of the Open Data Platform was WANdisco's joining as a founding member earlier this week. Other members already signed include CenturyLink, EMC, General Electric, Hortonworks, Infosys, Pivotal, SAS, Splunk, Teradata, Verizon, and VMware (source). WANdisco's contribution to the ODP is its replication technology. WANdisco's staff includes some of the original developers of Hadoop, and some senior core Hadoop committers.

However, some industry analysts are predicting that it is possible “that the market will have moved on from Hadoop by the time it really comes into its own.” Another Apache offering, Apache Spark, is positioning itself as a major competitor to Hadoop and already displacing it in many markets.

The effects of the ODP at this point are hard to measure. Big Data as a field changes so rapidly that the repercussions of a ripple, or even a big wave like the ODP, are difficult to track. In the long-run, the proof will be in the pudding. If Hadoop's dominance stays the course or grows rapidly we may be confident in assuming that it was the doing of ODP. If it shrinks, as some analysts predict--or is surpassed by another offering like Apache Spark--then we'll know that the ODP was not enough to keep the elephant alive.

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