Sun Tzu Interviews 8/10. “Those who use the military skillfully….” in.vu for the Enterprise to “Hadoop” Machine Data.
As the brilliant military strategist, Sun Tzu said 2,400 years ago in his seminal “Art of War“: “Those who use the military skillfully do not raise troops twice and do not provide food three times.”
On June 12, 2012, as the guest of Viglink, I attended ForumCon in San Francisco and I met Kevin Dodson, Co-Founder & CEO of In.Vu. His co-Founder is Jacques Nadeau, who is also the CTO. Kevin and Jacques are tackling machine generated big data overload with their prelaunch startup: In.Vu. Obviously this product is for the mid to large enterprise information technology (IT) departments.
What Does In.Vu Do? “Raising the troops” in modern times for an enterprise is mastering big data they generate. With enterprises producing machine data from websites, applications, servers, networks and mobile devices, with the frequency of synapses firing in the brain, there is simply too much data to be analyzed by humans. If you can “not raise troops twice and not provide food three times” you save lots of money. In.Vu searches and analyzes machine data using Hadoop.
What is Hadoop? According to Wiki: “Apache Hadoop is a software framework that supports data-intensive distributed applications under a free license. It enables applications to work with thousands of computational independent computers and petabytes of data.” Most importantly is that Hadoop works really well and it is free.
Product for the Enterprise. In.Vu is the machine data search and analysis tool for enterprise information technology operations. It works by sitting on top of Hadoop.
In.Vu, the Next Gen? In.Vu is easier to scale, because everything is on top of Hadoop, so massive archival storage and redundancy are built in. Plus, with In.Vu no SAN is required and we all know how expensives SANs are. In.Vu is more cost effective because of the ease of scaling. In.Vu has a non-propriety database because all of your data is in Hadoop, thus allowing access via the usual Hadoop tools of MapReduce, Hive or any of the new BI solutions built on Hadoop (Datameer, Karmasphere). Like war, in business, faster, cheaper and greater scale wins the battle.
Specific Example? If for example, Etsy were a beta client, In.Vu would do IT troubleshooting, security and compliance. The most important function would be the troubleshooting. If part of the Etsy site went down, in order to figure out the cause of the outage, the company would need to analyze each server logs for a hardware failure, network failure or application failure. Rather than searching each one of their servers individually, with In.Vu they could just search their monitored servers (a much smaller number of servers) with each monitored server available at a modest cost.
Takeaway: In the big machine data overload wars, raise your troops once and feed them once, use In.Vu to search and analyze your machine data.
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