At Glassbeam we are thrilled to ANNOUNCE our foray into the exciting space of machine learning and real time analytics. By integrating our market-leading Glassbeam SCALAR platform with the powerful capabilities of Apache Spark framework, we are adding significant differentiators to our IOT Analytics platform. This announcement received tons of media attention – here’s a nice BLOG POST by the Taneja Group.
The new capability will add a lot of cutting-edge machine learning algorithms to the Glassbeam repertoire: linear SVM and logistic regression, classification and regression tree and other cool capabilities. In less technical terms, it means that machines don’t (necessarily) have to be explicitly told on how to deal with incoming machine data – they can make their own scientific inferences on patterns congealed inside this data. Even better, by integrating Spark with our Cassandra instances, we will be able to offer Analytics in Real-time – as data is streaming in.
Let’s take an example: A manufacturer of computer networking devices uses Glassbeam’s machine data analytics solutions today to address support issue escalations; and reduces the Mean Time to Resolution (MTTR) by as much as 40% by using our IOT Analytics platform. With new machine learning algorithms, the customer will now be able to identify trends in incoming machine data and tag it with an “advanced warning” score of red/orange/yellow alerts – thereby alerting the user to an impending system issue or if it needs to be updated with new firmware etc.
With Apache Spark integration, our analytics engine will be lot faster (100X faster than Hadoop), more versatile (machine defined rules and alerts), more appealing, less resource-intensive (in-memory computing versus Disk I/O) and adding substantive new value to customers and partners. Which is why we believe we’re entering a new era with this addition to our repertoire of features.
All this has been possible due to lot of hard work on the part of our entire team and we’d love to take a moment to THANK and congratulate them for their tremendous work.