Tableau provides a refined set of tools to display and render data in more user-friendly formats, such as charts, graphs, and\u00a0visualizations. It is designed to connect\u00a0to multiple data sets, extract and filter selected data, and then provide that data to its visualization tools. Tableau can handle complex queries, but nothing can be analyzed or visualized effectively until the data has been located, acquired, refined, aggregated, protected and otherwise prepared for the visualization(s).\u00a0That is where CoSort (or Voracity) enters the scene.<\/p>\n
You\u00a0can extract, filter, transform, and mask data from\u00a0more than 150 different\u00a0data sources<\/a>. Without the need for Hadoop<\/a>\u00a0or DB engines,\u00a0the SortCL program<\/a>\u00a0in CoSort and Voracity does the heavy lifting of data\u00a0restructuring, transformation, and remapping. Its targets are custom-formatted, purpose-built subsets that\u00a0visualization applications easily\u00a0ingest. For privacy law compliance,\u00a0it can also apply field-level redaction, encryption, pseudonymization, tokenization, and other masking functions at the same time.<\/p>\n It should be noted that Voracity\u00a0and Tableau are not mutually exclusive. Both can be used to extract data from multiple sources and bring it into one location for reporting or further manipulation. However, depending on the data sources, data volume, platforms, and resources, CoSort or Voracity can be used with Tableau to pre-build a selected data set (flat file) as accepted input for analysis and creating visualizations.<\/p>\n An independent testing resource with expertise in Tableau was recently able to demonstrate the performance benefit of using CoSort as a pre-transformation tool for data feeds into Tableau. The focus of this test was only on the data collection tasks \u2013 sources, queries, filters, and generating the resulting data set. Time-to-delivery tests were conducted with CoSort against Tableau, which showed a dramatic performance difference with the sample inputs. Given the need to sort, join, and aggregate big data sources prior to producing reports in a BI tool, testing demonstrated comparative query and output speeds between the two products.<\/p>\n CoSort provides two ways for end-users to\u00a0acquire and stage data; via\u00a0a SortCL job script\u00a0run from\u00a0the command line, or in Voracity through\u00a0the IRI Workbench<\/a> GUI, built on Eclipse\u2122.\u00a0For the test, data from two large sources were sorted, joined, filtered, aggregated, and reformatted into CSV targets.\u00a0CoSort was run from the command prompt in this case:<\/p>\n The tester performed analogous integration steps to create the foundation for processing the same data files, and creating the same outputs in Tableau for visualization purposes:<\/p>\n![\"Tableau-Commands\"](\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2014\/09\/Tableau-Commands-e1409935858833.jpg\")
<\/a><\/p>\n
![\"Tableau_Join\"](\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2014\/09\/Tableau_Join.jpg\")
<\/a><\/p>\n![\"Tableau-Display\"](\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2014\/09\/Tableau-Display-e1409936545925.jpg\")
<\/a><\/p>\n