MACHINE DATA

Glassbeam for Glassbeam –Dogfooding and Loving it!

At Glassbeam, we have always believed in eating our own dog food and why not! We have the same use cases that our customers use for our platform. But, before I go deep into the internal use cases that we use Glassbeam for, let me explain how we collect our infrastructure logs and the types of logs we collect.

Glassbeam Lifts Gold as Most Innovative Company of the Year by Best in Biz Awards 2016

Recently, Glassbeam was named the most innovative company of the year by Best in Biz Awards 2016 International.  We were acknowledged due to our innovative approach at successfully developing unique machine data analytics solutions in the Internet of Things (IoT) market.

Welcoming dimension data to our family of customers

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:

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

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.

Predicting system failures to improve customer satisfaction

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?

Glassbeam for medical analytics – part 2

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.

Designing for the internet of things using cassandra

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

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.

Troubleshooting troubles! – part 2

Basic troubleshooting and Automation

In the previous blog, we looked at some of the steps commonly followed during troubleshooting and also how even though the specifics are different the overall approach is similar.

While looking at finding solutions to enable support, we need to remember that all support problems cannot be treated the same way. If we look at support issues, they are typically broken down into different levels, depending on the complexity of the problem. Most organizations have 3 to 4 levels – L1 – L3 or L4.

Search vs. Analysis on log data

Search as the starting point is a great way to start any analytics with Machine log data. As a user, initially you don’t know what you are searching for and hence searching for “needle in a hay stack” is easy, because all you need to do is type needle! Yes, you will get a lot of results back which then needs to be filtered/ranked and presented in a meaningful way, but open source search engines, that allow full text search of any document like SOLR/Lucene, provide a good starting point for search implementation.

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