On the heels of our groundbreaking 2.0 Release, the Vero team launches yet another major update, Vero 2.1. Now you can control more precisely how two blocks in Vero are blended. Set the join type and the join key. Additionally, the Vero data exported will now allow you to configure DDL options for your exported tables. This means you can easily specify primary keys and indexes for your tables. Faster visualizations are on the way!
Custom Joins Between Blocks
Blending just got a whole lot better! You can now decide how two blocks are blended and on which keys. Vero will use the keys submitted as hint when processing a blend amongst all the incoming data blocks. If you are a Tableau user, you will love this feature. Get past the blending limitations of "left join" only in Tableau.
- Include only data that exists in all of the sources. Use an Inner Join for this
- Include all data even if it doesn't exist in one of the blended sources. Use a Full Join here
- Vero auto joins two blocks based on commonly named Dimensions and Derived Dimensions. No you can specify which keys to consider in the join
Improved SQl table exports
Export data as materialized tables into one of the many data sources we support (i.e. Oracle, Postgres, MySQL, RedShift, Teradata, MS SQL Server). Vero handles the SQL Generation, Type Resolution, and Data Movement. With 2.1 you can configure indexes and primary keys for even faster visualizations.
Create blended physical reporting tables for your Visualizations. For big data, this would be faster than live querying across data sources
Complex calculations like Level of Detail (LOD) measures in Tableau, could significantly benefit from having the calculations done separately and stored in dedicated tables
Speed up your analytics and remove analytical load from your operational systems. Use Vero to move reporting data to a separate and dedicated database like RedShift
Have mission critical visualizations powered by large and big data? Cubes and data extracts provided by visualization tools often don't scale well. Instead of cubes and data extracts, move data to a dedicated mission critical visualization database