Come join our webinar on machine data security

Jul 28, 2014

At times, we encounter apprehension from customers, prospects, and partners about how secure end-customer data is on the cloud. To address, these concerns we’re hosting a Webinar with our Infrastructure-as-a-service partner, Dimension Data, next week. And we’ve lined up a star panel including our VP of Engineering, Ashok Agarwal and David McKenzie, Sr. Director, Solutions Architects at Dimension Data for you.


Welcoming dimension data to our family of customers

Jul 22, 2014

We’re thrilled to announce that Dimension Data has joined our fast-growing list of customers.

The Dimension Data deal is notable for a few reasons. It marks our foray into the Data Center Infrastructure management market – one that we think is primed for capitalizing on the various capabilities of our machine data analysis platform. And, that is exactly what Dimension Data is using Glassbeam for:

Our next meetup talks of big data technologies in glassbeam scalar

Jul 16, 2014

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.

Machine logs analytics – next frontier for data center infrastructure management (dcim)

Jun 25, 2014

There are few detractors when it comes to the value of DCIM for an Infrastrucutre-as-a-Service (IaaS) provider. But as the data centers become more dynamic and heterogeneous, these tools will need to adept. As CDW, a leading DCIM vendor PREDICTS that cross vendor visibility and heterogeneous platform will challenge the effectiveness of these tools.

Why phone-home makes strategic sense?

May 31, 2014

I have come across many sales situations where customers are unsure or wary of asking their customers to send regular feeds of the machine-generated data (logs). This feature is known as phone-home or call-home. The fact is that all high technology devices, systems and networks are constantly generating all kinds of log data (syslogs, configs, stats, static etc). This is now called the Internet of Things (IoT) phenomenon.

Predicting system failures to improve customer satisfaction

May 14, 2014

Companies like Amazon, Google, and Netflix have done an amazing job of providing a great customer experience. For example, when you use Google’s search engine, it quickly figures out if you are just researching a topic or planning to a buy a product/service. Accordingly, it tailors content to show you relevant ads or chooses not to display any. Similarly, when you buy a product on Amazon, it displays other products that may be of interest to you. Netflix offers suggestions of movies you may enjoy based on your viewing behavior. How do they do this?

Top predictions for 2014

Jan 08, 2014

Happy New Year – and what a year it’s been.

With the technology advancements of phones, tablets, glasses, clothing and machines over the last year, there are volumes of unstructured and multi-structured data collected from machines and connected devices, and it’s growing at a dizzying pace. The Internet of Things is quickly turning into the Internet of Everything – in today’s always-on, always-connected world, and machine data is everywhere.

Designing for the internet of things using cassandra

Sep 25, 2013

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 intersection of machine data analytics and the internet of things

Sep 08, 2013

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.

Build vs. buy dilemma in machine data analytics

Aug 29, 2013

Over the last few years of Glassbeam evolution, I have seen many of our customers and prospects grappling with the question of “build vs buy” when it comes to deploying a machine data analytics solution. It is no doubt intuitive for a product company to start thinking about building this as an in-house solution. Why? Because it is their products, log data, formats, and they know the best on what value they want to derive out of that. However, many of these home grown projects start with a big promise and deliver very little in the long term.