IBM is enhancing several of its existing software products with Apache Spark open-source technology.
Spark is now part of 15 separate IBM applications, ranging from various commerce apps to SPSS Advanced Analytics algorithms.
It uses Spark’s in-memory processing to simplify the architecture of some of its software solutions and cloud data services, such as IBM BigInsights, IBM Streams and IBM SPSS.
By using the technology, the company reduced the code base of its DataWorks service by more than 87%, from 40 million lines of code to 5 million lines of code.
The move resulted in simplifying operations and reducing build and deployment times.
IBM’s DataWorks will now benefit from Spark’s scalability, distributed programming model, and data source connectivity, as well as improvements delivered to it by the project’s contributors.
IBM Analytics for Apache Spark integrates with open source and third party tools on the company’s Bluemix cloud platform. Developers can infuse analytics into their apps in real-time.
IBM Cloud Data Services general manager Derek Schoettle said: "The availability of IBM Analytics for Apache Spark simplifies the process of getting started with Spark, letting data professionals focus on building apps instead of administering Spark clusters or managing operations.
"With integrations to key IBM Cloud Data Services, it’s easy for clients to build a complete solution on Bluemix and draw more insight from more data with less work."
IBM has made more than 60 contributions to the Spark project, including Machine Learning and SQL, since June this year.
This article is from the CBROnline archive: some formatting and images may not be present.
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