SANTA CLARA, CA — (Marketwired) — 11/18/14 — Glassbeam, Inc., the machine data analytics company, today announced a new version of Glassbeam SCALAR tightly integrated with Apache Spark, enhancing its cutting-edge Internet of Things (IoT) analytics platform with new capabilities around advanced machine learning and real-time analytics. These features strengthen Glassbeam offerings as it continues to maintain market leadership in ingesting, parsing and transforming complex machine data for some of the largest product manufacturers worldwide.
As the volume and variety of IoT-connected devices and machines grow dramatically, business users are demanding higher-velocity analytics on streaming data. Batch processing is simply too slow, creating the need for new ways to triage data as it flies in to produce actionable insights in real time.
Integrating the Apache Spark engine, Glassbeam is able to deliver a fast, in-memory distributed data processing framework for large-scale data — that is 100x faster than traditional Hadoop MapReduce architectures. To address the advanced analytics needs of its customers, Glassbeam is also adding new machine learning and predictive analytics capabilities to its core platform.
Glassbeam extends its value proposition for IoT and big data analytics
Glassbeam’s cloud-based analytics platform was designed for complex, multi-structured machine data and the new realities of business IoT. With these new capabilities, Glassbeam is now uniquely positioned as the only company to provide rich machine learning algorithms and real-time processing for complex machine data. For example, use cases will allow product manufacturers to become more proactive by preventing part failures, or predict which machines or parts are susceptible to higher failures rates in future. As a result, manufacturers will be able to prescribe solutions to problems before they happen in the field.
With a high-speed platform with distributed data and compute architecture, these levels of advanced analytics will translate into millions of dollars in cost savings through improved support and field service operations. By the same token, similar analytics will allow manufacturers to better understand customer and product behavior that will create new streams of services and up-sell revenues.
Specifically, the new version of Glassbeam SCALAR will provide:
- Superior performance and scalability: Apache Spark is a fast in-memory distributed compute architecture. When combined with Glassbeam SCALAR, it is the best of both worlds as Glassbeam SCALAR was architected with Cassandra as a distributed data processing architecture that not only scales linearly but also horizontally across thousands of nodes.
- Advanced analytics: With MLlib library integration from Apache Spark, Glassbeam SCALAR now has the industry’s best machine learning algorithms to perform predictive analytics on large sets of machine data in the cloud.
- Real-time analytics: Implementing Apache Spark SQL directly on Cassandra data will allow real-time analytics on data as it is streaming in and getting parsed and transformed through the Glassbeam SCALAR platform.
Glassbeam’s patent-pending, cloud-based technology enables customers to reduce costs, increase revenues, accelerate product time to market, and improve customer satisfaction and retention. Glassbeam customers and partners include Fortune 500 companies and enterprises across a variety of markets including storage, wireless, networking and medical devices.
“We’re seeing growing demand for real-time analytics as organizations seek to deliver richer insights to decision-makers and their partners and customers, faster. While the Internet of Things (IoT) may still be in its infancy, that too will require rapid analytics and machine learning capabilities. Since it is already tracking 1.2 billion sensor readings per day, Glassbeam has some expertise in this field, and we see its integration with the Apache Spark data processing engine as another step in the right direction.”
– Jason Stamper, Analyst, Data Platforms Analytics, 451 Research.