BIG DATA ANALYTICS

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

DINESH KATIYAR
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?

PUNEET PANDIT
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

MOHAMMED GULLER
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

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.

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”.

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.

Build vs. buy dilemma in machine data analytics

PUNEET PANDIT
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.

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.

Scaling machine data analytics for the internet of things – introducing glassbeam scalar

PUNEET PANDIT
Oct 21, 2013

from: Puneet Pandit, CEO, Glassbeam.

It’s a new era…

Welcome to the Internet of Things – where every connected physical device will generate volumes of machine data. And the key to extracting the intelligence and meaning locked inside this data will lie in a highly scalable platform and a set of applications. At Glassbeam, today marks the launch of that that truly next generation platform purpose-built for machine data analytics – Glassbeam SCALAR.

Big data: search vs. analysis

SRIKANTH DESIKAN
Oct 11, 2013

Early on in my career at SGI we built some interesting data management tools and data warehouses for processing world wide business data and BILL INMON was my hero(I am sure he was the hero for a lot of data geeks back then!).

This recent article on BIG DATA: SEARCH VS. ANALYSIS by him, is a great validation of the need for structure for analysis.

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