It’s been over year since the team at Glassbeam set out to build the next generation platform to analyze machine data generated by the "Internet of Things"( MORE BY HARBOR RESEARCH ON THE INTERNET OF THINGS HERE). And now, it is time to show the world the capabilities of this unique platform and solution set. READ MORE ABOUT "WHY GLASSBEAM?".
First SCALAR – a hyperscale platform designed to process streams and files of unstructured/semi-structured content. TANELI OTALA, OUR CTO, has combined his experience in real-time stream processing background from his days at Sensage, with Glassbeam’s core IP around its domain specific language SPL, to build something incredible.
Working with customers and prospects, we realized that many were struggling with the cost and complexities involved in processing multi-structured log files. Splunk has done a great job for IT admins through system logs in the data center. But the hidden value of logs from machines is tremendous especially for the product owners – namely business users in support, engineering, marketing etc at companies that manufacture these products. Glassbeam provide true Business Intelligence on machine logs benefitting product owners. See our VIDEO on why and how we address this important market.
SCALAR was an ambitious project and I am proud of our product and technology team based in Sunnyvale and in Bangalore, in creating a very flexible, scalable modern architecture to meet the complex demands of our market. Key benefits of this platform include
- Dynamic Schema: Handles complexity through dynamic schemas. We believe structure is critical for analysis and we are happy that the Guru of Datawarehousing, Bill Inmon RECENTLY VALIDATED it as well! But product logs can change with versions, new sections, events and attributes can be introduced and the platform has to gracefully handle new data elements without architecting a new schema, new datamarts and all the inefficiencies of old world business intelligence. SCALAR is designed to handle changes using a concept of incremental parsing and leveraging CASSANDRA.
- Context aware: Not everything in the log world is clean and neatly laid out as a csv or XML with clear metadata. Many times meta data has to be inferred, looked up, cross referenced etc from other sources, files or APIs. SCALAR provides a context language to do just that.
- High volume ingestion, parsing and loading: In the world where devices stream data constantly, the volume and variety of data is very high. SCALAR uses a parallel, asynchronous model to handle logs with a variety of sections and is capable of handling high volume of processing (parse, create schema on the fly, assign metadata and more). It scales horizontally and can be configured easily to work across several Cassandra nodes and/or Solr nodes.
- Defined APIs. : Creating structure is not enough, the data has to be exposed to the apps and analytics tools. This layer of SCALAR exposes all the data that is structured through defined APIs and provides simple plug and play into other enterprise apps.
Glassbeam also provides pre-defined apps such as Glassbeam Explorer and Analytics dashboards. Since our primary users are business users, these apps hide all complexity and provide a simple visual interface with intuitive filters, facets, charts and graphs to explore their data. We are very excited about our PARTNERSHIP WITH HDS(HITACHI DATA SYSTEMS) and that fact that Glassbeam provides them a competitive edge.
"Machine data is the data that never lies. Glassbeam takes a unique,trusted and proven approach to transforming raw machine data into predictive business intelligence. Using analytics to make more informed decisions, gain a deeper understanding of customer experience and generate new revenue opportunities can ultimately translate into a serious competitive edge for any business."- Analytics Services, HDS
The entire team at Glassbeam is very excited about this new launch as are several of our customers – To learn more, watch this VIDEO, DOWNLOAD INFORMATION from our website or give us a shout (@glassbeam on twitter).