Interview: Glassbeam Joins Forces with HDS for Complex Infrastructure Management

Today’s enterprise ecosystem brings with it unprecedented log data complexities. Glassbeam and Hitachi Data Systems have come together to bring multi-layered infrastructure management and log analysis to the enterprise data center. We caught up with Puneet Pandit, CEO and Founder of Glassbeam, to get the details.

insideBIGDATA: Glassbeam just had a major announcement concerning a partnership with Hitachi Data Systems (HDS). How did this relationship come about?

Puneet Pandit: HDS and Glassbeam have been go-to-market partners for a couple of years now. Our relationship started in 2012 when we jointly built “Health Check Services” for HNAS (Hitachi Network Attached Storage), that is a cloud based machine data analytics solution for HDS customers. We now have many joint customers in the market for these services. To further expand this footprint with HDS customers, HDS is now planning to OEM Glassbeam software and provide heterogeneous (beyond HDS storage assets) log analysis solution as part of their managed storage service (MSS) business. This move also has a strong strategic fit with HDS future direction as they move from infrastructure to content to analytics solutions with their enterprise accounts.

insideBIGDATA: What does this partnership mean from technological standpoint?

Puneet Pandit: From a technology standpoint, this new partnership announcement validates Glassbeam SCALAR platform capability to handle “big data” in the context of enterprise log data (lots of data, lots of variety, deep analytics). It also is a great proof point for the flexibility that Glassbeam has designed in the platform, that it can run on any cloud. This solution will ultimately be deployed in both HDS public cloud as well as private clouds at customer sites. For HDS, this will be a tremendous use case to deploy a search and analytics log solution in one stack on HDS infrastructure. It will help HDS provide “one stop” solution for their enterprise customers looking for operational and business intelligence from log data.

insideBIGDATA: What is machine data analytics exactly?

Puneet Pandit: Machine data includes all data generated by equipment, devices and sensors in data centers, telecom networks, industrial environments etc. The data in these machine logs contains valuable information like configuration, operational data, feature adoption and more that can be mined to provide actionable business intelligence. Machine data analytics allows you to parse, index, analyze and visualize this data to make sense of this machine logs.

insideBIGDATA: Why would an enterprise need multi-structured data analysis?

Puneet Pandit: Machine logs contain simple and complex data  – some logs contain time stamped data (i.e. syslogs) that are tactical events or errors used by sys admins to troubleshoot IT infrastructure. But other logs have more complex, unstructured or multi-structured text with sections on configuration info, statistics and other non-time stamped data. To make sense of the data in these logs, one needs a powerful language and processing engine to provide meaning and structure to the information. Once structure is defined, complex analytics and trend reporting can be performed.

insideBIGDATA: How will HDS implement Glassbeam’s log analysis solution and private cloud offering?

Puneet Pandit: HDS plans to deploy Glassbeam software on their infrastructure. This could be in HDS cloud or as an HDS appliance. Once an “appliance” specifications are built, tested and ready for production deployment, the solution can be rolled into private cloud offering.

insideBIGDATA: How will machine data analytics change the way your customers do business over the next, say, five years?

Puneet Pandit: Here are a few areas where machine data analytics will have a dramatic impact:

  • Proactive customer support that is tailored to meet a customer’s need based on their usage data.
  • Reduced burden on L2 & L3 support based on automated tools that resolve cases based on machine log data.
  • Improved product quality with the enhanced ability to machines to “tweet” their status allowing enterprises to figure out possible failures and alert for part replacement, fixes and repairs.

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