Informer Datasources & Datahub
Connect To All Your Data
Datasources are how Informer connects to your databases for data analysis. Anything that produces a JSON data stream — structured or unstructured — into Informer is a potential Informer Datasource.
There are so many possibilities!
In Informer 5, a Datasource does not have to comply with a rigid definition such as SQL, TCL/ECL, or other standards. Any ‘source’ can serve as a Datasource, including a traditional physical database, a REST API endpoint, a spreadsheet on your desktop, a proprietary data feed, or even a Twitter feed!
Datasource Drivers
An Informer Datasource is ultimately responsible for populating Informer Datasets. A Dataset is the elementary building block for Informer content and consists of a set of indexed Records. If you have a Datasource defined, you can create Datasets in one of two ways using: Informer Query Designer or Native SQL statements.
Connecting to your Datasource to create curated Datasets is accomplished using a Node.js based driver which is published for most modern database platforms and proprietary data stores. In the absence of such a vendor supplied driver, Informer developers can create one. Out of the box, Informer provides drivers for: Microsoft SQL Server, MySQL, Oracle, PostgreSQL, DB2, Progress OpenEdge, Generic JDBC, UniData, and UniVerse. Drivers are released for different types of databases over time and can easily be added by customers via plugins.
Adding and Editing a Datasource
Once connected, Informer scans your Datasource for tables and properties. For SQL-based databases, once the database is scanned, you can hide certain Mappings that have no relevance to data analysis or reporting. Hidden Mappings do not appear in the list of Available Mappings when creating Datasets or Queries.
See Figure 1: Datasource list page and menu for adding a new Datasource.
- Change Owner – The Datasource can be owned by individuals or a Team. Access to Team-owned Datasources is governed by the roles within the Team. Change the owner of the Datasource to another user or team
- Delete – Delete the Datasource
- Test Connection – This tests the connection between Informer and the database
- Scan – This rescans the database so if there is something new added or if you have unhidden some mappings/fields, they will show up after the rescan
- Export – This creates a package of the mappings, fields, and links
- Import Schema – This imports any packages already created
- Rename – Allows you to rename the Datasource
- Edit Connection – This screen allows you to edit the connection details
Figure 2
Workspace Datasources
Many times, organizations have different Comma Separated Value (CSV) files that hold pieces of the overall puzzle, but they find it challenging to create a single view to extract meaningful value. For example, there may be separate CSV files for each sales department region and you want to bring them together into one Workspace for analysis.
Workspaces allow collections of one or more related Comma Separated Value (CSV) files into an Informer Datasource which can be linked, queried, typed, etc. all the same as any other Datasource.
As you import a CSV file into a Workspace, Informer:
- Scans for columns headers
- Intelligently guesses at data type for each column
- Provides the ability for the user to reconfigure the types
- Creates a PostgreSQL table per the document structure
Once a CSV successfully imports into a Workspace, it exists not as a file but as an actual table in the local Informer PostgreSQL database for data analysis. In this way, it is manipulated as any other Datasource. So, as you add associated CSV files into the same workspace, you can create links provided the files contain logical associations.
