Machine data can contain various types of unstructured information ranging from events to configuration and statistical information. Trouble shooting wireless devices involves running several CLI(Command Line Input) commands and analyzing the output for patterns. An access point may be rebooting, a process may be consuming unusual amounts of memory, a controller may have some incorrect settings, ports may be shutdown etc. The number of variables a support person in a product company, selling wireless devices, needs to track from the logs is so large and dispersed across various CLI outputs that manually going through this is very time consuming. Our wireless customers use Glassbeam to parse these files intelligently so business rules can be built on top of the parsed data to identify common patterns. A collection of such patterns are then made into a summary report dramatically reducing the time to problem resolution. All this is possible only because there is technology to take highly random structured or multi-structured machine data and make sense out of it in an automated way.
This is an example of a highly complex Big Data( High Variety) problem involving parsing, ETL and data modeling done with an advanced language called SPL( Semiotic Parsing Language).