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September 2, 2015

SAP adds an analytics Spark to HANA

Big Data Spark shocks IBM mainframes into life.

By James Nunns

The growing popularity of Spark has seen it gain a new supporter in SAP with HANA Vora.

The software will allow customers to combine business data that is stored in its HANA database with other sources of data that are stored in Spark, such as industrial sensor data.

HANA Vora, an in-memory query engine, is designed to leverage and extend the Apache Spark execution framework in order to provide interactive analytics on Hadoop. The company isn’t limiting its use to a single industry, believing it to have use cases across various areas.

Aziz Safa, VP & GM, IT enterprise applications and application strategy, Intel, said: "One of the key requirements for us is to have better analyses of Big Data, but mining these large data sets for contextual information in Hadoop is a challenge."

SAP is also planning to expand its HANA Cloud Platform’s ability to push digital transformation as it increases digital connectivity to enable businesses to make the most of the growing IoT market.

Customers and partners will be able to add device management along with device data connectivity and bi-direction device data synchronisation, all with the goal of enhanced digital connectivity.

With the company aiming to expand its share for its HANA product, it is an important move to embrace technologies like Spark, which others such as IBM, Microsoft and Oracle have all opted to support.

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IBM has already committed more than 3,500 researchers and developers to Spark and its Series z mainframes are also being connected with the Apache Spark technology.

Syncsort released an open source tool that helps to connect the mainframes with the Big Data technology. In theory, this will help to bring mainframe users up to date with modern day analytics.

With mainframe users often being locked in for decades to their architecture this technology could help to make the data analysing element modern and up to date.

The connector will help companies to pull centralised mainframe data into their distributed Big Data systems, making it easier to analyse.

With technology like this, the death of mainframes looks even further away, particularly when you consider that around 80% of the worlds corporate data is still managed by mainframes.

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