I have recently started to hear about Prism, which utilizes Column storage and this ‘cool’ just-in-time in-memory processing it has got. This weekend, I got a chance to play around and do some basic research on it. Here are my first few observations.
Web demo provided on their website looked neat, clear and seemed like an easy to read interface. I found Prism demo less crowded with basic colors but still very readable or as they like to call it ‘intuitive’. Seemed like an interface for efficiency or as windows phone commercials say it: Get-in, Get-out. I liked the ability to highlight, add subtotals, sort visualizations etc on the go for better understanding.
In addition to filters the ability to filter out all the visualizations based on selection on one, helps to quickly analyze and understand relationships on the go. Although for now, it seems selections made in visualizations can be changed only by clicking at a little icon that pops when you hover your mouse over that visualization. But sometimes it can be difficult to figure out where selection has been made and you would have to hover over each and every chart and click to remove selection. I have not looked much into it but it might already have another way to remove selections.
“Prism stores all the data it processes in ElasticCube data repositories, which are column-based data stores containing the unified data of all combined data sources.”
“Prism performs all query processing on data which is loaded into memory only when it is needed for a query”. My concern to this just-in-time in-memory is the performance compromise on this addition step of loading data every time however it does mean no more compressing and loading of all data and in-memory saving tons on hardware and need to reload the whole data if there are any additions or changes in data model.
“Prism’s data storage and handling is based on an “elastic data structure,” ….. Virtual data merging and multi-source data abstraction are performed automatically by the software, allowing the user to create “data mash-ups” across data from multiple sources, effortlessly.”
- Good price-quality ratio
- Self-service, no programming or scripting (SQL scripting available if desired), drag and drop report creation
- Query against of data files, ODBC-compliant databases, OLAP cubes and cloud data sources
- Column storage based, high performance central data repository
- Zero foot print web browser-based analysis
Although it does lack the ‘sticky’ feeling and might need more visualizations to be added to stack but no doubt Sisense has thought clear and far enough to put together a tool with a solid foundation and is equipped with latest technology to handle scalability and performance.