Analytic workloads are growing, and for businesses to remain competitive, they must be able to efficiently and securely analyze their data to generate usable insights. Key to this is performing analytics at the source of origin of the data, without the costly, time-consuming and risky ETL process. Enterprises seek efficiency and cost-effectiveness as well as the ability to leverage portable modern programming languages and open source technologies without needing to learn new, platform-specific skills in order to generate insights.
Recognizing these needs, last year IBM and several partners announced the IBM z/OS Platform for Apache Spark. The platform combines the benefits of Apache Spark analytics with an optimized data integration layer supporting data abstraction for real-time access to high-value business data. This was the first step in enabling clients to speed time to insights while minimizing data security risks and keeping costs down. Today we are building on that foundation by bringing Anaconda, powered by Continuum Analytics, to the mainframe. In the same way that Apache Spark opened the door for big data analytics on the mainframe for Scala and JavaScript programmers, the Anaconda stack of technologies gives that same access to Python programmers.
The enhancement to the platform to support Anaconda natively on z/OS is flexible and extendable. We are doing this in collaboration with two IBM Business Partners with deep expertise in analytics, Continuum Analytics and Rocket Software. The Anaconda stack is powered by Continuum Analytics. Rocket Software helps enable Anaconda on z/OS for customers to leverage for client solutions.
This partnership will help clients running z/OS to get even more out of their transaction data. Not only can they now analyze data at the place of origin and without the need to know COBOL, but it also opens the mainframe to new workloads (Python, Java, Scala, R) and a new generation of users who before may not have had the skills needed to analyze z/OS data.
This extended partnership with Continuum Analytics and Rocket is just one of several ways IBM is making the mainframe even more of a business asset. Not only can the system process massive volumes of data, but it can do so through a variety of channels and provide valuable insights based on those analytics efficiently.
To learn more about the value of running real-time analytics on IBM z Systems visit http://www-03.ibm.com/systems/z/solutions/real-time-analytics/.
–Mythili K Venkatakrishnan, z Systems Analytics Technology & Architecture Lead, IBM
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