Machine data or “data exhaust” analysis is one of the fastest growing segments of “big data”–generated by websites, applications, servers, networks, mobile devices and other sources. The goal is to aggregate, parse and visualize this data – log files, scripts, messages, alerts, changes, IT configurations, tickets, user profiles etc – to spot trends and act.
By monitoring and analyzing data from customer clickstreams, transactions, log files to network activity and call records–and more, there is new breed of startups that are racing to convert “invisible” machine data into useful performance insights. The label for this type of analytics – operational or application performance intelligence.
In this posting we cover a low profile big data company, Splunk which recently went public. Splunk has >3500 customers already. Splunk ended its first day on the stock market with amazing 108.7 percent bump in price from its $17-per-share IPO.