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IBM Boosts Mainframe; Adds Spark for Real-time Insights

Monday, April 11, 2016

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FREMONT, CA: IBM adds Spark, an open-source analytics framework, to run natively on its IBM z/OS Platform for Apache Spark. The new offering makes it easier and faster for data scientists and developers to access and analyze data in-place on the IBM z Systems mainframe to deliver real-time insights.

With IBM z/OS Platform for Apache Spark, Spark runs natively on the z/OS mainframe operating system enabling users to analyze data on the mainframe without having to do extract, transform and load (ETL) or break the connection between the analytics library and underlying file system. Data scientists can take advantage of z mainframe Systems data and capabilities to understand market changes and individualized client needs and perform business adjustments in real-time, reducing time and increasing value.

The z mainframe systems from IBM feature the fastest commercial processors and the capability to perform in-transaction analytics, scoring predictive models within a transaction in 2 milliseconds or less.

The IBM z/OS Platform for Apache Spark includes Spark open source capabilities consisting of the Apache Spark core, Spark SQL, Spark Streaming, Machine Learning Library (MLlib) and Graphx, combined with the mainframe-resident Spark data abstraction solution. The platform enables enterprises to maximize value and extract the potential from the derived insights by allowing users to utilize the following features such as streamlined development, Simplified data access, In-place data analytics and Open source capabilities.

Streamlined development

Developers and data scientists can use their existing expertise with programming languages such as Scala, Python, R and SQL to reduce time to value for actionable insights.

Simplified data access

Optimized data abstraction services remove complexity, providing seamless access to enterprise data in traditional formats such as IMS, VSAM, DB2 z/OS, PDSE or SMF with familiar tools via Apache Spark APIs.

In-place data analytics

 Apache Spark uses an in-memory approach for processing data to deliver results quickly. The platform includes data abstraction and integration services that enable z/OS analytics applications to leverage standard Spark APIs. This allows the organization to analyze data in-place, avoiding costly processing and security considerations associated with ETL.

Open source capabilities

The platform offers an Apache Spark distribution of the open source, in-memory processing engine that is designed for big data.

IBM is also working with three partners, DataFactZ, Rocket Software and Zementis, to create customized solutions using IBM z/OS Platform for Apache Spark:

DataFactZ is a new IBM partner that is working with Big Blue to develop Spark analytics based on Spark SQL and MLlib for data and transactions processed on the mainframe. Rocket Software is helping to extend z/OS to Apache Spark by enabling its clients to use its Rocket Launchpad solution on z/OS. Zementis is complementing its in-transaction predictive analytics offering for z/OS with an execution engine for Apache Spark.

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