MACHINE DATA ANALYTICS

Learn-teach-repeat: apache solr workshop @ glassbeam

SAUMITRA SRIVASTAV
Jun 24, 2014

At Glassbeam, we use many different open source technologies. Our stack consists of Apache Cassandra, Apache Solr, Apache Spark, Scala, Play, H2 to name few. We try to give back and share with the community. We call it learn-teach-repeat initiative.

Another great industry recognition for glassbeam

Jun 02, 2014

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".

Data security on the glassbeam cloud

ASHOK AGARWAL
May 28, 2014

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.

Glassbeam for medical analytics – part 2

VIVEK SUNDARAM
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

PUNEET PANDIT
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

SRIKANTH DESIKAN
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

ASHOK AGARWAL
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

SRIKANTH DESIKAN
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.

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

ASHOK AGARWAL
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.

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