Return on investment (roi) from log analytics solutions

Apr 29, 2014

The question of ROI comes up quite regularly in our sales discussions with prospects. It is natural for our buyers to delve on that topic since many evaluate GLASSBEAM SPEND WITH INTERNAL EFFORTS. We are no doubt on an evangelistic mission to convert naysayers inside product companies who think they can "build" such a solution on their own.

Typically there are three parts of GLASSBEAM VALUE that constitute a very compelling ROI:

Glassbeam for medical analytics – part 2

Feb 07, 2014

In PART ONE of the Glassbeam for Medical blog, we explored how Glassbeam helps customer support save turnaround time and avoid unnecessary replacement costs. Support is one use case that benefits from machine data analytics. But the benefits of such a solution can span across the enterprise from customer support to engineering to sales and marketing.

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.

Glassbeam begins where splunk ends – going beyond operational intelligence with iot logs

Dec 10, 2013

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.

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”.

Glassbeam for medical – analyzing mri machine logs

Vivek Sundaram
Sep 18, 2013

The advent of complex medical devices has triggered a renaissance of sorts and imposed a new set of demands on the healthcare industry. Intelligent medical equipment now connect to a central location, enabling key stakeholders to glean insights from historical data collected over time.

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.

Why dynamic columns make sense for glassbeam architecture

Aug 14, 2013

Glassbeam Engineering is working heads down on our next-gen architecture using Cassandra and related column family structures. There are many reasons for this evolution, but one of the key drivers is a compelling support use case. Here is some background for this topic.

An auto-tuning parser for data from the internet of things (iot)

Nov 15, 2013

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.