To use the Incorta Profiler, you have to install these four components:
IncortaDataPrep Python Package (Required)
The sample data and data folder is an optional step since you will need to replace the data source in the materialized views with your own schema tables. However, we recommend you deploy the sample data to verify your deployment and understand how it works by trying it out with the sample data.
Two data sets are used as samples
House Price Prediction
Step 1: Install the DataPrepAPI Wheel File
Here is how to install the IncortaDataPrep wheel file in the on-premises environment:
First, copy the file to your Incorta on-premise server.
Step 2: Upload the Data_Profiler_Datasets data files
The Titanic Survivors and House Price data sets are packaged as it is used as the sample data.
This step is optional. You can instead use your own datasets.
Please note that the data files are stored under the folder Data_Profiler_Datasets.
Step 3: Import Schema
In the Schema, we have five MVs:
All these MVs call the DataPrepAPI Python package functions so installing the Python package is required for validating and saving the MVs.
If you don’t deploy the sample datasets Data_Profiler_Datasets as mentioned in the prior step, You need to open the MVs and change the full load and the incremental load logic by pointing to your data source.
In freq_item, both the full load and incremental load, in the screenshot highlight part change to your [SCHEMANAME].[TABLENAME]
Please switch off the incremental logic, run the schema load job before editing the incremental logic.
Edit Incremental logic:
If you have multiple data sources, you can add them in incremental logic using unionAll
Make the schema table name change with the format [SCHEMANAME].[TABLENAME] for both the full load and incremental load for all five MVs: table_info, summary_table, correlation_table, histogram, and freq_item
Step 4: Import Dashboard
Step 5: Add Session Variables (Optional)
The Session Variable is used to display the default table in the list of Table Names.