Via its acquisition of Compose, a provider of managed services spanning multiple types of databases, and the general release today of a managed dashDB Enterprise MPP service based on the IBM Netezza and IBM BLU Acceleration database technologies, IBM is making it clear that it wants to manage massive amounts of data on behalf of customers.
Derek Schoettle, general manager of IBM Cloud Data Services, says IBM ultimately plans to provide multiple services around three primary modes of managed data services. The first is around the database itself. The second is providing access to analytics applications hosted by IBM. Finally, IBM will also provide access to analytics data that the company processes as part of a larger business process.
The consumers of that data, adds Schoettle, will be traditional business analysts that IBM has always targeted as well as both professional and citizen developers that need to access data. In fact, Schoettle says providing access to large amounts of data as a service will be critical to accelerating the development of the overall API Economy.
To that end, Schoettle says Compose will extend types of databases that IBM can effectively manage as a service to include MongoDB, Redis, Elasticsearch, PostgreSQL and RethinkDB alongside the Cloudant, IBM DB2 and dashDB Enterprise MPP services that IBM already supports.
Hosted on the IBM Bluemix platform, dashDB Enterprise MPP provides access to built-in Netezza analytics libraries along with integration with Watson Analytics, R, Cognos and third-party BI toolsets including Looker, Aginity Workbench and Tableau. Compatible with both Oracle and Netezza applications, dashDB MPP is meant to provide a high-scale, massively parallel processing (MPP) engine that can be invoked on demand.
Separately, IBM today also announced that it is making the Apache Spark in-memory engine for analytics applications available on IBM mainframes, which Schoettle notes complements an existing Apache Spark service that IBM delivers via Bluemix.
Finally, Schoettle says that IBM will take advantage of Docker container expertise to expand the number of database platforms it exposes as a service.
Put it all together, and IBM is clearly aiming to become an analytics powerhouse in the cloud at a time when demand for analytics expertise is increasing at a rate with which the existing supply of analytic talent simply can’t keep pace.