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Procore

Export to SQL Server Using Python (SSIS)

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Overview

The Procore Analytics Cloud Connect Access tool is a command-line interface (CLI) that helps you configure and manage data transfers from Procore to MS SQL Server. It consists of two main components:

  • user_exp.py  (Configuration setup utility)
  • delta_share_to_azure_panda.py (Data synchronization script)

Prerequisites

  • Python and pip installed on your system.
  • Access to Procore Delta Share.
  • MS SQL Server account credentials.
  • Install required dependencies: pip install -r requirements.txt.

Steps

Initial Configuration

  • Run the configuration utility:
    python user_exp.py

This will help you set up the following:

  • Delta Share source configuration
  • MS SQL Server target configuration
  • Scheduling preferences

Data Synchronization

After configuration, you have two options to run the data sync:

  1. Direct Execution python
    delta_share_to_azure_panda.py
    OR
  2. Scheduled Execution
    If configured during setup, the job will run automatically according to your cron schedule.

Delta Share Configuration

  1. Create a new file named config.share with your Delta Share credentials in JSON format.

{
"shareCredentialsVersion": 1,
"bearerToken": "xxxxxxxxxxxxx",
"endpoint": "https://nvirginia.cloud.databricks.c...astores/xxxxxx"
} 

 

  1. Get required fields:
    Note: These details can be obtained from the Procore Analytics web application.
    • ShareCredentialsVersion: Version number (currently 1).
    • BearerToken: Your Delta Share access token.
    • Endpoint: Your Delta Share endpoint URL.
    • Save the file in a secure location.
  2. When configuring the data source, you'll be asked to provide:
    • List of tables (comma-separated).
    • Leave blank to sync all tables.
    • Example: `table1, t able2,  table3`.
    • Path to your `config.share` file.

MS SQL Server Configuration

You'll need to provide the following MS SQL Server details:

  • database
  • host
  • password
  • schema
  • username

SSIS Configuration

  1. Using  the command line, navigate to the folder by entering 'cd <path to the folder>'.
  2. Install required packages using 'pip install -r requirements.txt' or 'python -m pip install -r requirements.txt'.
  3. Open SSIS and create a new project.
  4. From SSIS Toolbox, drag and drop 'Execute Process Task' activity.

    analytics-data-connector-sql-ssis.png
  5. Double-click on 'Execute Process Task' and navigate to Process tab.
  6. In 'Executable', enter the path to python.exe in python installation folder.
  7. In 'WorkingDirectory' enter a path to the folder containing  the script you want to execute (without script file name).
  8. In 'Arguments' enter the name of the script 'delta_share_to_azure_panda.py' you want to execute with the .py extension and save.

    analytics-sql-ssis2.png
     
  9. Click on 'Start' button in upper pane:
    analytics-sql-ssis.png
  10. During the execution of the task, output of the Python console is displayed in the external console window.
  11. Once the task is done it will display a green tick:

    analytics-sql-ssis1.png