Data transformations

Editing transformations locally

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.

You can either customize the transformations from within Process Mining using Data transformations, or you can edit them locally on your desktop, see Editing data transformations in a local environment

This page describes how to customize data transformations from within Process Mining. Data transformations are integrated in the dashboard editor. See Working with the dashboard editor.

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 KPIs. For example, Case_has_maverick_buying.

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.

Opening Data transformations

1. Select the Data transformations button in the upper right corner of the the dashboard editor to open the Data transformations.

Note:

If you open the in-line editor for the first time, it will take some time to load the editor.

The in-line data transformations editor is displayed.

Input data

The input data panel shows the input tables that have been loaded into the process app as raw data. Select a table to see the fields and the data contents in the data preview. The data preview enables you to check if the input data looks as expected.

Note:

The preview shows 1000 records of data. If you want to filter for specific data, create a temporary debug query, see Data transformations.

Note:

The Preview panel shows the data of the last data run in which this table was recalculated. If you have made recent changes, then start a new data run to view its results, see Editing and testing data transformations.

Transformations

1. Select Transformations to view the structure of the transformations and to display the .sql files.

See Structure of transformations for more information on the structure of the transformations.

Each .sql file defines a new table in the data model. If you select the a .sql file in the Transformations panel, the SQL query is displayed in the Transformations and a preview of the data file you are editing is displayed in the Preview panel. See the illustration below for an example.

When editing a query, you can see a preview of the data of the last time the query was run in the Preview panel.

Important:

If there are any unsaved changes in the transformations, the Save button is enabled. Select Save to save the changes. The status of the transofrmation will be set to Up to date.

Viewing and editing the data model

Important:

Always make sure your data model adheres the requirements. See Data model requirements.

1. Select Data model to view the data model of your process app.

See Editing and testing data transformations for more information on how to change the data model.

Validation checking

A notification is displayed if your data model does not meet the requirements.

See Data model requirements.

Adding a table

1. Select + Add table. The Add table dialog is displayed.

2. Select the table that defines the new output table.

3. Select the Primary key for the new table, and select Done.

See Adding tables for more information on how to add a table in the transformations.

Adding relations

1. Select the table that you want to relate to another table.

   The Edit table panel is displayed.

2. In the Edit table panel, select + Add new to create a new relation.

3. Select the field that you want to use in this table from the Key list.

4. Select the Table you want to connect to and select the field that use to connect from the Key list.

5. Select Apply.

Changing the key for a table

1. Select the table for which you want to change the key.

   The Edit table panel is displayed.

2. In the Edit table panel, locate the relation for which you want to change the key.

3. Select the new field that is to be used as the Key to join the tables.

4. Select Apply.

Note:

If you change the primary key, the field that you select as the new primary key is duplicated in the table. You can delete the original field in Data Manager.

Deleting a relation

1. Select the table for which you want to delete an outgoing relation.

   The Edit table panel is displayed.

2. In the Edit table panel, locate the relation you want to delete and select Delete relation.

3. Select Apply.

Deleting a table

1. Select the table that you want to delete in the data model editor.

   The Edit table panel is displayed.

2. Select Delete table.

   A confirmation message is displayed.

3. Select Delete to continue.

The table and the relations are deleted from the data model.

Making the new data model available for use in dashboards

1. Select Save to save the data model.

2. Select Apply to dashboards to run the transformations and make new the table available for use in dashboards.

   Note:

   This may take several minutes. If the run finishes successfully, the changes to the data model will show up in the Data Manager.

Note:

The Save option is only enabled, after you made any changes to the data model.

Viewing the transformations Log

The Log panel shows what happens in the background. The Log panel is refreshed automatically every couple of seconds. See the illustration below for an example.

Log levels

The log contains different log levels to indicate the severity of the message:

Log level	Description	Data run status
Information	An Information message contains helpful information on the progress of the datarun.	Success
Warning	A Warning refers to a potential problem in your data that might affect what will be displayed on a chart in the published process app. It is advised to resolve any warnings to prevent any potential future problems.	Success
Error	An Error refers to a mistake in your data that prevents the process app from loading the data or running the transformations. You must resolve all errors to enable a successful data run.	Failed

Filtering logs

You can use the Filter menu to change the log level. See the illustration below for an example.

Saving the log file

You can select the icon to copy the contents of the log file and paste in, for example, a Notepad file that you can save on your computer. This enables you to view the messages when working on solving the issues causing errors or warnings.

Note:

This also enables you to share the contents of the log file, for example if you need support.

Editing and testing data transformations

Editing transformations

Refer to the pages listed below for more information on editing data transformations.

• Structure of transformations

• Designing an event log

• Tips for writing SQL

Running the queries

Attention:

Run queries is limited to 10 minutes. Therefore, it is strongly recommended to use a small dataset for the development and testing of data transformations, to make sure your transformations do not exceed this time limit. See also: Process Mining portal.

When you are editing transformations, you can select Run queries to test your changes and see a preview. It runs the queries that were modified queries and all related queries. For example, if cases_input.sql has been modified. The Run queries command would run cases_input.sql and cases.sql, which uses the results of cases_input.sql.

The resulting data will only be available in the Data Transformations editor, not in the dashboard editor.

There are two types of runs:

• Run queries: starts a data run, recalculating all modified queries, and their dependencies.

• Run file: starts a run to recalculate only the currently selected SQL file and its parent queries. This option enables you to test and debug customizations to specific SQL files.

Tip:

You can select Cancel run at any time to cancel the transformations run.

The results are displayed in the Preview panel.

Note:

Run queries is typically much faster than Apply to dashboards which starts a full data run.

Note:

Run queries does not affect the data in the published process app.

Adding folders and files

You can create new folders and add new files in your transformations.

Naming conventions

• For names of files, folders, fields, and tables:

  • you can only use lowercase (a-z) characters, uppercase (A-Z) characters, and numbers (0-9);

  • you cannot use special characters |*:?"<>\/ for names of files, folders, fields, and tables.
• For file names, you can only use _:. as special characters.
• A file name cannot start with an _ (underscore).

Creating a new folder or file from the transformations menu

Follow these steps to create new folder or file.

Steps	Action
1	Navigate to the location in the transformations where you want to add a new file or folder.
2	Go to the Transformations panel and select the menu icon to open the transformations menu.
3	Select the applicable option from the menu.
4	Enter a descriptive name for the new folder or file and select Enter.

The new folder or file is created in the selected folder.

Creating a new folder or file from the context menu

Follow these steps to create new folder or file from the context menu.

Steps	Action
1	Right-click on the folder in the transformations where you want to add a new file or folder.
2	Select the applicable option from the menu.
3	Enter a descriptive name for the new folder or file and select Enter.

The new folder or file is created in the selected folder.

Unsaved changes in files

When you edit a .sql file it is indicated in the Transformations folder structure that there are unsaved changes.

In the above example there are unsaved changes in the Tags.sql file that is in the models -> 5_business_logic folder.

Renaming a folder or file

Follow these steps to create new folder or file.

Steps	Action
1	Right-click on the folder or file you want to rename to open the context menu.
2	Select Rename.
3	Edit the name as desired and select Enter.

The folder or file is renamed.

Deleting a folder or file

Follow these steps to create new folder or file.

Steps	Action
1	Right-click on the folder or file you want to delete to open the context menu.
2	Select Delete. A confirmation message is displayed.
3	Select Delete to confirm the deletion.

The folder or file is deleted.

Adding comments in files

If you want to include comments in your SQL transformations files, or comment-out parts of the file, it is strongly recommended to use the Jinja comment syntax which is by default set to {# ... #}. This also enables you to comment macros.

SQL

{# {{ pm_utils.optional(ref('Cases_base'), '"Case_status"') }} as "Case_status", #}

{# case
    when {{ pm_utils.optional(ref('Cases_base'), '"Case_value"', 'double') }} >= 1000000 
      then {{ pm_utils.as_varchar('>= 1M') }}
    when {{ pm_utils.optional(ref('Cases_base'), '"Case_value"', 'double') }} >= 0 
     then {{ pm_utils.as_varchar('0 - 1M') }}
   when {{ pm_utils.optional(ref('Cases_base'), '"Case_value"', 'double') }} is not null
     then {{ pm_utils.as_varchar('< 0') }}
  end as "Case_value_group", #}

Find / replace in files

In the Data Transformations editor, you can find and replace texts inside a single file.

Find text

To find a text in the data transformation editor:

1. Click on a random place in the code editor, or select the text you want to search for other occurrences.

2. Press CTRL+F.

3. Enter the test you want to search for in the Find field.

   All occurrences of the text in the current file are highlighted.

4. Select ENTER to skip to the next occurrence.

Find and replace

To find a text in the data transformation editor:

1. Click on a random place in the code editor, or select the text you want to search for other occurrences.

2. Press CTRL+H.

3. Enter the test you want to search for Find field and enter the new test in the Replace field.

   All occurrences of the text in the current file are highlighted.

4. Select ENTER to replace the text and skip to the next occurrence.

To find a text in the data transformation editor:

1. Click on the code editor.

2. Press CTRL+H.

3. Enter the test you want to search for Find field and enter the new test in the Replace field.

   All occurrences of the text in the current file are highlighted.

4. Press Crtl+Alt+Enter.

Tip:

You can also use the Replace or Replace all icons.

Find/replace in multiple files

If you want to Find/replace texts in multiple files, you can only do this in an external editor.

Follow these steps to Find/replace texts in multiple files:

1. Export transformations from your process app.

   Note:

   If you have pending changes in the Data Transformations editor, first use Apply to dashboards, and export the transformations after the data run is completed.

2. Unzip the transformations.

3. Open the transformations in your favorite code editor, for example, Notepad++, or Visual Studio Code.

4. Use the functionality of those tools to find or replace texts in multiple files.

5. Save the files.

6. Add the transformation files to a .zip file.

7. Import the transformations in your process app.

Note:

When you are asked which data to use for a data run, select Cancel if you want to continue editing transformations before doing a new data run.

Editing dbt project configuration files

You can also open dbt project configuration files in the Data Transformations editor, for example dbt_project.yml.

Warning:

It is strongly advised to only edit the vars section, if required. The other parts of the dbt_project.yml should not be changed to ensure the transformations keep working properly.

For detailed information on dbt, see the official dbt documentation.

Making the transformations available in dashboards

To make the changes available in the dashboards, you must execute a full data run to load the data and run the transformations. Select the Apply to dashboards button to start a data run.

Note:

Depending on the size of your dataset, this make take some time.

After a successful data run, the new transformations become available in the dashboard editor and can be used to adjust the dashboards.

Attention:

If you have a published version of the process app, business users will instantly see the new data. If you want to make sure that your published app keeps running properly while editing transformations, it is strongly advised to use a development app.

Follow these steps.

1. Clone your app to create a development app.

2. Edit and test the transformations.

3. Export the transformations from the development app.

4. Import the transformations in your published process app.

Merging event logs

If your event log is split over multiple event log files, you can merge the event logs into a single event log in Data transformations.

Note:

To merge multiple event logs, the Case_ID should point to the same object for all files. For example, all Case IDs are pointing to Sales Order IDs., such that Case_ID points to the same table for all event logs.

1. Create a new app, based on the Event log app template. See App templates.

2. Use the Upload data option in the Selecting the Data Source step, and select the event log files you want to merge.

3. Continue the Create new app wizard to create the app and to transform the data.

   The data run will fail and the Application status will show Run failed.

4. Select View log to view the log file.

   An error message is displayed indicating that the expected Event_log_raw.sql file is not found.

   

5. Go to the Data transformations editor.

6. For each input file:

   1. Create a .sql file with the same contents as the already available Event_log_input.sql.

      

   2. Adjust the file to select the required input fields and type cast them correctly.

   3. Add the name of the new source tables to sources.yml.

      Now all new input files can be combined into a single event log.

      Attention:

      Make sure that the input files all have the exact same set of fields.

7. Select the Event_log_input.sql file and replace the contents with:

   -- The following code merges 3 input tables.
   select * from {{ ref('Event_log_input_1') }}
   union all
   select * from {{ ref('Event_log_input_2') }}
   union all
   select * from {{ ref('Event_log_input_3') }}

   
   Note: Adjust the code to match the names of the input tables needed.

8. Select Apply to dashboards to run the data transformations and make the resulting table available for use in the dashboards.