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information optimization

Next Generation Analytics

Relational Databases

Data Visualization Tools for Relational Databases

Datawatch's visual analysis tools are ideal fits with data stored in relational databases like Oracle, DB2, Microsoft SQL Server, and Teradata.Datawatch's visual analysis tools are ideal fits with data stored in relational databases like Oracle, DB2, Microsoft SQL Server, and Teradata.Relational databases (also called "row-oriented databases" or "row stores") are the most common type of DBMS and are part of almost every organization's IT infrastructure. Datawatch visual analytics tools can connect to virtually all standard relational databases, including DB2, Microsoft SQL Server, Oracle, Teradata, and MySQL.

Build SQL queries into your dashboards

Datawatch's ability to incorporate complex SQL queries into your dashboards means that you can call up information stored in your RDBMS and visualize it in many different ways. You can utilize the heirarchy found in the structure of your relational data or alter it on-the-fly as needed to help you identify outliers and patterns in your data.

Fast data connections to the most popular relational databases

Datawatch includes data connectors for the most popular databases built into the system. This vastly simplifies initial installation of the system and helps your people start using visual analytics tools with their data in a matter of minutes. Datawatch comes equipped with data connectors for:

  • IBM DB2
  • Microsoft Access
  • Microsoft SQL Server
  • MySQL
  • Oracle
  • PostgreSQL
  • SAP Sybase ASE
  • Teradata

Federate data from a relational database with other sources in a single visualization

Use Datawatch to federate data from a relational database with other sources, including flat files like Excel with data from real-time message queues, CEP engines or column-oriented tick databases. You use simple Excel-like formulas to combine data in a Datawatch dashboard. Even single information visualizations like Treemaps can include data pulled from multiple sources.