InetSoft BI Webcast: Data Grid Cache Map-Reduce for Business Intelligence

Data Grid Cache

Mark: So this is the label we’re giving to the technology that we’re introducing in this release to deliver performance improvements and establish a path for scaling up for accessing massive databases while we’re providing service to a large number of simultaneous users, and Byron’s going to add a little more description to this for you.

Byron:  Thanks Mark.  Traditionally, our software has always defaulted to accessing your operational data sources directly.  We really espoused the idea of traditional BI, where you’re required to build a data warehouse.  The only problem with that, and it only comes up some of the time, is that operational data sources tend to be attuned more for data entry and writing.

For instance, a CRM system where information is added into the system.  So they’re not performance oriented as much for reporting or the reading of that data especially for analysis - interactive analysis and visualization of that data.

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Some of our customers have built reporting databases where they’re simply replicating the data over.  Some even go so far as building a data warehouse, although our software doesn’t require that.  So, we introduced the concept of materialized views a few versions ago and with this latest release we’ve really reengineered and re-architected it all so it’s much higher performance.

The core of it is based on techniques from column based databases or data stores that are much more efficient for interactive analysis of large amount of data and if you have really large volume of data we’ve also built in distributed technology that’s largely based on the map reduce concept where we can parallelize a lot of calculations on extremely large data sets making use of a cluster or private cloud of commodity hardware instead of forcing you to invest in a very large, high performing machine.

So with those two technologies, the new data grid cache feature really extends this embedded materialized view concept even further and turns the whole process of data warehouse development on its head; where you would first build out the reports in viewsheets and then analyze the contents of the viewsheet to see how you’re using the data, now we can build an appropriate column based data store that will make that particular dashboard very high performing.

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