- Release notes
- Before you begin
- Getting started
- Integrations
- Working with process apps
- Working with dashboards and charts
- Working with process graphs
- Working with Discover process models and Import BPMN models
- Showing or hiding the menu
- Context information
- Export
- Filters
- Sending automation ideas to UiPath® Automation Hub
- Tags
- Due dates
- Compare
- Conformance checking
- Root cause analysis
- Simulating automation potential
- Triggering an automation from a process app
- Viewing Process data
- Creating apps
- Loading data
- Customizing process apps
- App templates
- Additional resources
- Out-of-the-box Tags and Due dates
- Editing data transformations in a local environment
- Setting up a local test environment
- Designing an event log
- Extending the SAP Ariba extraction tool
- Performance characteristics
Setting up a local test environment
To edit data transformations, a local test setup is recommended for an improved edit and test experience for data engineers.
|
Tool |
Usage |
|---|---|
|
dbt |
to execute the data transformations on a local database. |
|
Visual Studio Code |
to edit the SQL code of the data transformations. |
|
SQL Server (Express edition) |
for testing the data transformations. |
|
SQL Server Management Studio |
for reviewing the result of the data transformations in the local database. |
dbt project, you need Python 3.9. You can download Python 3.9 from the official Python website.
It is recommended to create a Python virtual environment in which you will install dbt. It is good practice to create a dedicated folder for your transformations. You can also use this folder to create the Python virtual environment.
|
Step |
Action |
|---|---|
|
1 |
Open Windows Explorer and create a folder where your virtual environment will be located. For example,
C:\My_transformations.
|
|
2 |
Open a Windows Command Prompt. |
Run the commands described in the steps below to create a Python Virtual environment.
|
Step |
Action |
Command |
|---|---|---|
|
1 |
Install the Python package
virtualenv.
|
|
|
2 |
Go to the folder where you want to create the environment. |
cd [path_to_your_folder]. For example, cd C:\My_transformations |
|
3 |
Create a virtual environment (named
venv).
|
|
Make sure the virtual environment is still activated. Follow these steps to install dbt to run transformations.
Running scripts must be enabled on your system. Follow these steps:
-
Open PowerShell in Administrator mode.
-
Enter the command Set-ExecutionPolicy RemoteSigned or Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser.
-
Enter Y when prompted.
|
Step |
Action |
Command |
|---|---|---|
|
1 |
Activate the virtual environment. |
|
|
2 |
Install the dbt package |
pip install dbt-sqlserver==1.4.3*
|
|
3 |
Check whether the installation is successful |
|
dbt 1.4 feature to include Python scripts as part of your dbt project.
Visual Studio Code is the recommended code editor for editing data transformations.
You can download Visual Studio Code from Download Visual Studio Code webpage.
After you have installed Visual Studio Code, install the following extensions to make it easier to work with dbt:
- Dbt Power User
Follow these steps to install an extension in Visual Studio Code.
|
Step |
Action |
|---|---|
|
1 |
Start Visual Studio Code |
|
2 |
Go to the Extensions panel (CTRL+SHIFT+X). See the illustration below. |
|
3 |
Search for the dbt Power User extension by start typing the name in the Search Extensions in Marketplace text box. |
|
4 |
Click on Install. |
See the illustration below.
To run Python from the virtual environment that was created, the path needs to be set in Visual Studio Code.
Follow these steps.
|
Step |
Action |
|---|---|
|
1 |
Go to File > Preference > Settings. |
|
2 |
Search for Python. |
|
3 |
In the Default Interpreter Path, make sure to check the path to the
python.exe. It should be located in the
[path_to_your_folder]\venv\Scripts folder. See the illustration below for an example.
|
Microsoft SQL Server is the required database to test the transformations. This database server is not provided as part of the Process Mining product. For editing and testing the transformation you can also use SQL Server Express. You can download Microsoft SQL Server Express from the official Microsoft SQL Server downloads webpage.
By default, SQL Server is case insensitive whereas Process Mining is case sensitive. You are advised to change the behavior of your local SQL Server database to match this behavior, to prevent any problems. This can be accomplished by setting the right collation during installation. The default value for the collation is dependent on your locale.
CI part with CS, e.g. Latin1_General_CI_AS should be changed to Latin1_General_CS_AS. If you already installed SQL Server, follow instructions describe in Set or change the server collation to update the collation.
Below is an overview of the minimum requirements for the SQL Server for a local development environment. If you want to use more than 10M records in your development environment, please use the Capacity calculator to calculate the corresponding system requirements for the SQL Server.
| Type | Minimum requirements (10M events) |
|
Version | SQL Server 2019 or above |
|
CPU | 8 vCPU/cores (16 recommended) |
|
Memory | 32 GiB RAM |
|
Disks | Low latency & dedicated disks are recommended |
|
Data disks | 256 GiB |
| Temp DB | 128 GiB |
You can use the Capacity calculator to determine the hardware requirements for setting up a dedicated Microsoft SQL Server machine for Process Mining. See Capacity calculator.
To view the database tables and to manage the SQL Server infrastructure, you can download SQL Server Management Studio (SSMS), which can be installed on any computer.
