BIG DATA

At Glassbeam, we are big fans of Functional Programming and Scala. SCALAR our hyper scale big data platform was built ground up using Scala. Additionally, we utilize the AKKA actor framework for asynchronous and distributed processing. We truly believe that SCALAR as an evolutionary platform for organizing and analyzing complex machine data in the era of the Internet of things (IOT). In case you missed it, here is the SCALAR launch ANNOUNCEMENT.

Big data Applications are no longer a nice to have, but must have for many organizations.Many enterprises are already using massive data being collected in their organizations to understand and serve their customers better. Ones which have not yet learned how to use it will over a period of time, be left behind. Companies are now looking for platforms which not only provide them analytical capabilities over their data, but also help them become PROACTIVE AND PREDICTIVE. And hence machine learning capability is becoming an important component for any analytical platform.

Growing a sales organization is a double edge sword ! It brings more prospects in the pipeline, but it also creates demand on marketing and executive management to crystallize the core value proposition very quickly. I am not complaining :-) I think it is great that I have been pulled into some critical pre-sales discussions recently where I had to think on my feet, white board and create new slides to help sales close new deals. So, this blog is is a multi-series blog that highlights our core message and value propositison to the market we sell into.

It is an honor to be recognized (again) by CRN in their TOP 50 LIST OF COMPANIES that are changing the world of business analytics with big data. To quote CRN, "this list recognizes companies that have demonstrated an ability to innovate in bringing to market products and services that help business work with big data".

You can’t stop the NSA – no matter what; but rest assured, the “Target” type of security breach resulting in the theft of credit card information has nothing to do with the cloud.

Not all wars are bad. Surprisingly, there are wars that most individuals and groups enjoy – wars over pricing. Nothing is better than watching some of the biggest companies punching each other out with a “new pricing strategy”. This is a war that is good for the customer since the end result is an increase in goods delivered at a new reduced price.

Recently you might have been a part of a conversation that went something like:

"Can we reduce our IT infrastructure cost by 10…15% this year?"

"No boss, only by 30…40%"

"Are you insane? How?!"

Splunk announced today that it acquired Cloudmeter to enhance its capability to analyze machine data. While tools like Splunk have tremendous value for internal data centers, companies are starting to embark on more complex Big Data projects with multi-structured machine logs. The rapidly exploding market of physically connected devices and the machine data generated by the “Internet of Things” is driving the need to leverage this data beyond simple troubleshooting needs.

I like the word “ontology”. It has a nice ring to it. Wikipedia defines Ontology as “knowledge as a set of concepts within a domain, and the relationships among those concepts”. When applied to machine data analytics (“domain”), we see that unless we isolate concepts and understand the relationships, we cannot obtain “knowledge”.

The ability to gather and harness data from machines and devices connected to the net is increasingly becoming a source of competitive advantage for those who have been thinking ahead. GE is widely cited, with their INDUSTRIAL INTERNET initiative. Cisco has been talking about INTERNET OF EVERYTHING for a while. At Glassbeam, we have been focusing on the infrastructure required to process and analyze machine data for over 3 years now.

We have established beyond a reasonable doubt that knowledge comes from structure. Therefore, parsing IoT logs to create structure is a must do for making sense of this data. Remember the definition of big data – volume, variety and velocity. If you combine that with the business requirement of near real-time analytics, you are looking at a need for high data ingestion speeds. However, the issue is not of ingestion speeds but the total cost of ownership (TCO) for providing that.