Architecture

The main problem we faced early on was that the default language used under the covers, in just about any business intelligence user facing tool, is SQL.  At first glance it seems that the worlds of data integration and SQL are not compatible. In Data Integration we read from a multitude of data sources, such as databases, spreadsheets, NoSQL and Big Data sources, XML and JSON files, web services and much more. However, SQL itself is a mini-ETL environment on its own as it selects, filters, counts and aggregates data. So we figured that it might be easiest if we would translate the SQL used by the various BI tools into Pentaho Data Integration transformations. This way, Pentaho Data Integration is doing what it does best, not directed by manually designed transformations but by SQL. This is at the heart of the Pentaho Data Blending solution.

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To ensure that the automatic part of the data chain doesn't doesn’t become an impossible to figure out black box, Pentaho made once more good use of existing PDI technologies. All executed queries are logged on the Data Integration server (or Carte server) so you have a full view of all the work being done:

Server Execution and Monitoring

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