To edit data transformations, a local test setup is recommended for an improved edit and test experience for data engineers. A local test environment includes the following set of tools.
|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.|
To install and run a
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.
|1||Open Windows Explorer and create a folder where your virtual environment will be located. For example, |
|2||Open a Windows Command Prompt.|
Run the commands described in the steps below to create a Python Virtual environment.
|1||Install the Python package |
|2||Go to the folder where you want to create the environment.|
|3||Create a virtual environment (named |
Make sure the virtual environment is still activated. Follow these steps to install dbt to run transformations on Snowflake.
|1||Activate the virtual environment.|
|2||Install the dbt package|
|3||Check whether the installation is successful|
The returned message should start with:
installed version: 1.1.2.
Visual Studio Code は、データ変換の編集に推奨されるコード エディターです。
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.
|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.|
To run Python from the virtual environment that was created, the path needs to be set in Visual Studio Code.
|1||Go to File > Preference > Settings.|
|2||Search for Python.|
|3||In the Default Interpreter Path, make sure to check the path to the |
Microsoft SQL Server is the required database to test the transformations. This database server is not provided as part of the UiPath 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.
It is recommended to test the transformations using small development datasets. This makes it possible to test these using a SQL Server with minimal requirements. If you do not have a Microsoft SQL Server available, or if you want to test the transformations on a local desktop machine, it is recommended to use Microsoft SQL Server Express.
By default, SQL Server is case insensitive whereas Snowflake is case sensitive. You are advised to change the behavior of your local SQL Server database to match Snowflakes 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.
To change the collation to become case sensitive, replace the
Latin1_General_CI_ASshould be changed to
Latin1_General_CS_AS. If you already installed SQL Server, follow instructions here to update the collation.
You can use the 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.