When we last left Oracleâ€™s Big Data plans, there was definitely a missing piece. Oracleâ€™s Big Data Appliance as initially disclosed at last fallâ€™s OpenWorld was a vague plan that appeared to be positioned primarily as an appliance that would accompany and feed data to Exadata. Oracle did specify some utilities, such as an enterprise version of the open source R statistical processing program that was designed for multithreaded execution, plus a distribution of a NoSQL database based on Oracleâ€™s BerkeleyDB as an alternative to Apache Hive. But the emphasis appeared to be extraction and transformation of data for Exadata via Oracleâ€™s own utilities that were optimized for its platform.
As such, Oracleâ€™s plan for Hadoop was competition, not for Cloudera (or Hortonworks), which featured a full Apache Hadoop platform, but EMC which offered a comparable, appliance-based strategy that pairs Hadoop with an Advanced SQL data store; and IBM, which took a different approach by emphasizing Hadoop as an analytics platform destination enhanced with text and predictive analytics engines, and other features such as unique query languages and file systems.
Oracleâ€™s initial Hadoop blueprint lacked explicit support of many pieces of the Hadoop stack such as HBase, Hive, Pig, Zookeeper, and Avro. No more. With Oracleâ€™s announcement of general availability of the Big Data appliance, it is filling in the blanks by disclosing that it is OEMâ€™ing Clouderaâ€™s CDH Hadoop distribution, and more importantly, the management tooling that is key to its revenue stream. For Oracle, OEMâ€™ing Clouderaâ€™s Hadoop offering fully fleshes out its Hadoop distribution and positions it as a full-fledged analytic platform in its own right; for Cloudera, the deal is a coup that will help establish its distribution as the reference. It is fully consistent with Clouderaâ€™s goal to become the Red Hat of Hadoop as it does not aspire to spread its footprint into applications or frameworks.
Of course, whenever you put Oracle in the same sentence as OEM deal, the question of acquisition inevitably pops up. There are several reasons why an Oracle acquisition of Cloudera is unlikely.
1. Little upside for Oracle. While Oracle likes to assert maximum control of the stack, from software to hardware, its foray into productizing its own support for Red Hat Enterprise Linux has been strictly defensive; its offering has not weakened Red Hat.
2. Scant leverage. Compare Hadoop to MySQL and you have a Tale of Two Open Source projects. One is hosted and controlled by Apache, the other is hosted and controlled by Oracle. As a result, while Oracle can change licensing terms for MySQL, which it owns, it has no such control over Hadoop. Were Oracle to buy Cloudera, another provider could easily move in to fill the vacuum. The same would happen to Cloudera if, as a prelude to such a deal, it began forking from the Apache project with its own proprietary adds-ons or substitutions.
OEMs deals are a major stage of building the market. Cloudera has used its first mover advantage with Hadoop well with deals Dell, and now Oracle. Microsoft in turn has decided to keep the â€œcompetitionâ€ honest by signing up Hortonworks to (eventually) deliver the Hadoop engine for Azure.
OEM deals are important for attaining another key goal in developing the Hadoop market: defining the core stack â€“ as weâ€™ve ranted about previously. Just as Linux took off once a robust kernel was defined, the script will be identical for Hadoop. With IBM and EMC/MapR forking the Apache stack at the core file system level, and with niche providers like Hadapt offering replacement for HBase and Hive, there is growing variability in the Hadoop stack. However, to develop the third party ecosystem that will be vital to the development of Hadoop, a common target (and APIs for where the forks occur) must emerge. A year from now, the outlines of the marketâ€™s decision on what makes Hadoop Hadoop will become clear.
The final piece of the trifecta will be commitments from the Accentures and Deloittes of the world to develop practices based on specific Hadoop platforms. For now they are still keeping their cards close to their vests.