Glassbeam SCALAR - analysis of the internet of things
The internet of things could be very helpful to organizations or bury them under the weight of operational data from a herd of devices, their software, and their applications. Glassbeam believes its SCALAR is the tool that will make sense of it all.
The company is building its machine learning and predictive analysis tools on top of the Apache Spark in-memory, highly scalable, distributed database engine to provide extremely fast data analysis. The goal is to provide organizations with real-time analysis of just about any type of data their devices might be generating.
Here's what the company has to say about its product
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 hearing more and more about the internet of things computing environment and how it is likely to impact the IT organization. Some of the real effects IT will experience remain to be seen. What is clear is that staff and customers are using more and more different types of devices to access the organization's workloads and those workloads are being hosted on a growing array of different types of systems, networks and storage devices.
IT is expected to be able to quickly detect performance and security anomalies and get to the root cause. Wading through all of the log files produced by all of the devices, their operating systems, the application frameworks and the databases in use could easily become a time-consuming chore that would prevent a timely discovery and resolution response.
Many performance monitoring and management companies have jumped into the market with their own approach to turning what appears to be a huge, growing mass of different type of structured and non-structured data into something useful to IT administrators. Glassbeam believes that its approach will be the best.
I'd suggest taking the time to speak with other suppliers of tools, such as Splunk, Sumo Logic, LogLogic, Loggly and a few others before making a selection. It may turn out that Glassbeam will be the best fit to address your organization's needs. It also may turn out that one of the others will be a better fit.