You are viewing content for 4.4 | 4.3 | Previous Releases


Data Hub

Data Hub is a tool that enables Incorta to act as a PostgreSQL database, enabling users to utilize Incorta’s powerful engine performance and features, even through other BI tools (e.g. Tableau, MicroStrategy, Power BI). For example, Users can choose to load their data into the Incorta engine (memory) so that they can take advantage of its robust performance. Or, they can opt to load their data into Incorta’s staging area (if the data is too large for the Incorta assigned memory), and access this data from other BI tools through the SQLi port, set in the admin UI.

Refer to the following table for an overview of the pros and cons of Data Hub.

Scenario Pros Cons
All Schema tables are loaded in Memory Analyze users can build any Insight they want with flexibility. Consumes more memory.
All Schema tables are loaded in Memory No need for technical database (e.g. SQL) expertise. Degraded performance when building Insights, since querys will be executed at runtime.
Schemas built on Data Hub Enhanced performance when building/editing Insights, since the querys will only be executed at the time of loading a schema. Analyze users do not have the flexibility of building an Insight without referring to the schema manager.
Schemas built on Data Hub No need for large memory. Query tables will cause schemas to take a long time to load.

Important Terms

Before proceeding, it is important that you become familiar with the following terms:

Loaded Source Schema, contains all the tables from the data source (e.g. Oracle, MySQL). This schema can be loaded:

  • Fully in memory.
  • Partially in memory, and partially in the staging area.
  • Fully in the staging area.

Loaded Query Schema. This schema contains only the query tables for Analyzer users when building dashboards. These queries will depend on the business user’s requirements, and must be created by users with the “Schema Manager” role.

Connect to Data Hub from external BI tools

In order to connect to Incorta’s Data Hub from external BI tools (e.g. Tableau), use the following steps:

  1. Create a connection to Data Hub.
  2. In the external BI tool, provide the following parameters:

    • Data Source type: Choose “PostgreSQL” as your data source type.
    • Data Source Name: Provide the tenant name used in Incorta.
    • Username/Password: Provide Incorta’s login credentials in the appropriate fields.
    • Connection string: Provide a connection string including Incorta’s host server, port number (5436 to connect to Incorta’s engine, or 5442 to connect to Data Hub), and the tenant name.
  3. Discover the source schema tables created in Incorta.

Now you are ready to build dashboards in your BI tool.

Create a New Data Source Connection

In order to deploy Incorta with Data Hub, the first step is to create a connection with Incorta as PostgreSQL, using the following steps:

  1. Select +New to open the Add New Data Source window.

  2. Provide the following parameters:

  3. Data Source type: Choose “PostgreSQL” as your data source type.

  4. Data Source Name: Provide the tenant name used in Incorta.

  5. Username/Password: Provide Incorta’s login credentials in the appropriate fields.

  6. Connection string: Provide a connection string including Incorta’s host server, port number (5436 to connect to Incorta’s engine, or 5442 to connect to Data Hub), and the tenant name.

  7. Select Add Data Source. The Test Connection button appears.

  8. Select Test Connection. The connection test status window appears.

  9. Select OK.

Create a query schema

As mentioned before, a query schema contains query tables. Depending on the business user’s requirements, users with the “Schema Managers” role can build query schemas based on the query tables they create, and load them.

In this step, Schema Manager can use the schema wizard to discover tables located in the source schema initially created in Incorta.

Once the query schema is properly defined and loaded in Incorta’s memory, users with “Analyzer” role can easily use it to build Insights.