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Process Mining

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Automation Cloud Automation Cloud Public Sector Automation Suite Standalone

Last updated Nov 13, 2024

Editing data transformations in a local environment

Introduction

Note:

It is strongly recommended to customize data transformations from within Process Mining using the Data Transformations editor. See Data transformations for more information on how to customize data transformations from within Process Mining.

This page describes how to customize data transformations outside Process Mining.

When you create a process app from an app template, you want to make sure the data used in the process app reflects your business process in the correct way. With Data transformations you can customize the data used for Process Mining.

Data transformations enable you to:

add new fields for group by and filters. For example, Quality check type.
add events. For example, Invoice quality check done.
add tags. For example, Failed to meet SLA,Four eyes violation.
add business logic for calculating KPI's. For example, Case_has_maverick_buying.
define new input fields for calculations.

Data Transformations editor

You can either customize the transformations from within Process Mining with Data Transformations, or you can export the transformations from Process Mining and edit and test them using a local test environment.

Note:

Editing the transformations using the Data Transformations editor in Process Mining does not require a local development environment.

Versioning Transformations

Currently, there is no versioning of transformation code in the product. When new transformations are imported, the previous ones get overwritten. It is not possible to revert to the previous version of the transformations.

Note: It is recommended to keep local copies of previous versions of the code when making changes. To track changes, transformations on your local machine can be versioned in a versioning system like GIT or SVN. That way, you can roll back to a previous version using the versioning system, and Import those transformations into the product when needed.

Prerequisites

When starting editing data transformations it is strongly recommended that you:

have in-depth knowledge of SQL;
are familiar with the source system that is used for data extraction.

Permissions

You need Edit transformations permission for the process app for customizing data transformations.

See Managing access for process apps.

Tools

A code editor is required for editing data transformations. Visual Studio Code is the recommended code editor for editing data transformations. You can download Visual Studio Code from Download Visual Studio Code webpage.

It is recommended to run and test the data transformations outside Process Mining, before you import the edited transformation in your process app. To run and test the data transformations, a local test environment is required.

See Setting up a local test environment.

Customizing the transformations

Each process app has a corresponding set data transformations. Customizing data transformations requires several steps to be performed.

Export the transformations from the process app.
Open the transformations in Visual Studio Code.
Run the transformations*.
Edit the transformations.
Test the transformations in a local test environment.*
Import and test the transformations in a test process app in Process Mining.
Import the transformations in the process app.

* Running and testing the transformations on a local test environment are optional steps.

1. Export the Transformations From the Process App

See Exporting transformations.

2. Open the transformations in Visual Studio Code

Follow these steps to open the transformations in Visual Studio Code.

Step	Actions
1	In Windows Explorer, create a folder for the exported transformations in the folder where your virtual environment is located. For example, `C:\My_transformations\TemplateOne-MultiFiles`.
2	Unzip the exported transformations `.zip` file in the folder.

See the illustration below for an example.

Step	Action
3	In Visual Studio Code, go to File -> Open Folder... and select the folder that contains the unzipped transformations.

See the illustration below for an example.

The dbt project is interpreted.

Date and time formats

Important:

Snowflake uses different date and time formats than SQL Server. If date and time variables are defined for the transformations for your process app, you must change the date and time formats in dbt_project.yml to the format required by SQL Server. Before you create the .zip file with the new transformations, you must change the date and time formats back to the format required by Snowflake.

If the date and time formats are not defined in your dbt_project.yml file, the default values are used.

For SQL Server date and time formats are defined by integers and for Snowflake defined by strings.

Below is an overview of the different (default) formats for SQL Server and Snowflake.

variable	SQL Server format	Snowflake format
`date_format`	23	'YYYY-MM-DD'
`time_format`	14	'hh24:mi:ss.ff3'
`datetime_format`	21	'YYYY-MM-DD hh24:mi:ss.ff3'

3. Run the transformations

Important: Running the transformations is only applicable if you want to test the transformations outside Process Mining on a local test environment.

It is good practice to run the transformations before you start editing the transformations. This enables you to check whether the set up is correct and the data is loaded correctly.

Before you begin

To run the transformations you must:

create a new database schema for the process app. You can do this in Microsoft SQL Server Management Studio. Follow these steps.

Step	Action
1	In the Object Explorer in Microsoft SQL Server Management Studio, go the folder of your database in the Databases folder.
2	Right-click on Security and select New -> Schema...

See the illustration below.

See the official Microsoft documentation for more information.

load data in the new schema and make sure that the required input tables are available in the schema. You can either load data using an extractor load data directly from .csv files in Microsoft SQL Server Management Studio. Follow these steps

Step	Action
1	In the Object Explorer in Microsoft SQL Server Management Studio, locate your database in the Databases folder.
2	Right-click on the name of your database and select Tasks -> Import Flat File...

Note: It is recommended to use a small dataset for testing data transformations. The lower the record count of the dataset, the faster transformations will be executed.

Set up Profiles.yml

When you open a dbt project in Visual Studio Code for the first time, a dbt message is displayed, indicating that the profile corresponding to your new transformations cannot be found. See the illustration below for an example.

Note: If you start editing transformations, you must first set up a profiles.yml file that is connected to your Microsoft SQL Server database. For new transformations, you can extend the profiles.yml file by adding a new entry.

Below is a template for the profiles.yml file, or click the link to download the template: profiles.yml.

my-app-template:
  outputs:
    default:
      type: sqlserver
      driver: 'ODBC Driver 17 for SQL Server'
      server: 
      port: 
      user: 
      password: 
      database: 
      schema: 
      encrypt:
      trust_cert:
      
  target: defaultmy-app-template:
  outputs:
    default:
      type: sqlserver
      driver: 'ODBC Driver 17 for SQL Server'
      server: 
      port: 
      user: 
      password: 
      database: 
      schema: 
      encrypt:
      trust_cert:
      
  target: default

Step	Action
1	Open a text editor. For example Notepad++.
2	Copy and paste the content from the template above.
3	Replace `my-app-template` with the `profile` name as displayed in the `dbt_project.yml`. For example `uipathTemplateOne`. See the illustration below.
3	Edit the file, such that it points to the schema that was just created.
4	Save the `profiles.yml` file in the folder that does not contain a dbt project (see screenshot below). For example, `C:\My_transformations`.
5	Create an environment variable called DBT_PROFILES_DIR with the file path of the folder that contains the `profiles.yml` file.

Note: By default, the source data is stored in the schema where the transformations run. If your source tables are in a different schema, you can define this schema in the schema_sources variable.

See the official dbt documentation for more information on how to configure your profile.

Running the transformations

Follow these steps to run the transformations.

Step	Action
1	In Visual Studio Code, right-click on the models folder and select Open in Integrated Terminal from the context menu.
2	Enter `dbt build` at the command prompt in the integrated terminal.

4. Edit the transformations

For guidelines on editing transformation see Transformations and Tips for writing SQL.

5. Test the transformations on a local test environment

Important: Test the transformations is only applicable if you want to test the transformations outside Process Mining on a local test environment.

Follow these steps to test the transformations.

Step	Action
1	Enter `dbt build` at the command prompt in the integrated terminal in Visual Studio Code.
2	Check the data in SQL Server Management Studio to verify the values. Right-click on a table and select Select top 1000 rows. If necessary, modify the query to get the desired results.

Edit and test the transformations on a local test environment until the transformations run without any errors and the data is correct.

6. Import and Test the Transformations in a Test Process App in Process Mining

It is strongly recommended to run the new transformations in a test process app in Process Mining before importing the transformations in the original process app. Since the transformations are tested on a local SQL Server environment, errors may occur when running the new transformations in Process Mining.

Step	Action
1	In Windows Explorer, go to folder where your transformations are stored For example, `C:\My_transformations\TemplateOne-MultiFiles` and add the transformations to a new `.zip` file.
2	Go to the Process Mining portal, and create a new process app based on the same app template as your process app.
3	Import the transformations in the test process app.
4	Check the dashboards to see if the data is presented correctly.

7. Import the Transformations in the Process App

Important:

When the transformations are imported the new transformations are automatically run. This will immediately affect the data displayed in the published process app. It is strongly recommended to test the new transformations in a separate test process app to prevent any errors (see Step 6: Import and test the transformations in a test process app in Process Mining).

Running an erroneous transformation will result in the published app not being visible for end users.

See Importing transformations.

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