With Apache Spark integration and Spark-as-a-Service, IBM will combine its propietary analytics offerings with the open-source versatility of Spark.
IBM has redesigned over 15 of its core analytics and commerce solutions to incorporate Apache Spark. In an October 26 press release, the company also announced the Spark-As-A-Service offering on the IBM Bluemix cloud platform after a successful beta program.
Spark, which is open source, is known for its ability to easily create algorithms that allow data sets to be processed and analyzed quickly. IBM said that Spark has allowed it to simplify the architecture of its commerce and analytics products, including BigInsights for Apache Hadoop, InfoSphere Streams, and SPSS predictive analytics.
“The power and appeal of open source innovation for technologies like Spark is undeniable,” Rob Thomas, vice president of product development for IBM Analytics, said in a press release. “IBM is committed to using Spark as the foundation for its industry-leading analytics platform, and by offering a fully managed Spark service on IBM Bluemix, data professionals can access and analyze their data faster than ever before, with significantly reduced complexity.”
RTInsights Take: As the amount of data grows from Internet of Everything sensors and the Web, the ability to stream and analyze data is of paramount importance, as it enables revenue-boosting opportunities in predictive maintenance, energy production and use, marketing, healthcare, and a variety of other fields. Enterprises, however, have struggled with streaming and getting a handle on fast data. The combination of IBM’s commercial analytics offerings with the versatility of Spark would appear to be a powerful one.
IBM provided two use cases for Spark:
- DataWorks, a data preparation and refinement service, used Spark to reduce its code base by 87 percent, shrinking 40 million lines of code to 5 million.
- SolutionInc, a Nova Scotia-based company that provides WiFi and wired internet services to hotels, conference centers and hotspots around the world, uses Spark to rapidly process and analyze massive amounts of WiFi data. The company used IBM Spark to explore 240 million rows of Wi-Fi log information and identify device traffic patterns and data across multiple locations.
Want more? Check out our most-read content:
Frontiers in Artificial Intelligence for the IoT: White Paper
Five Big Data Trends: Emerging Technologies
Why Machine Learning Is Crucial for Predictive Maintenance
Deciphering the Payments Dataverse in Real-Time
Liked this article? Share it with your colleagues!