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Cerner

Last updated: 05-29-2025

Welcome! This is an early release of the Cerner connector.

The Cerner Connector is a dbt project that maps Cerner's Millennium Data Model to the Tuva Input Layer, which is the first step in running The Tuva Project. Ultimately, this connector will help you transform your data into a structured, analytics-ready format using the power of dbt and the Tuva Project's healthcare data models.

We can think of this project as a specialized pipeline:

  1. Input: Cerner EMR data located in your data warehouse.
  2. Process: Uses dbt (data build tool) to clean, transform, and model this raw data. dbt allows us to write data transformations as simple SQL SELECT statements and handles the complexities of dependency management, testing, and documentation.
  3. Framework: Leverages The Tuva Project, an open-source project providing standardized data models specifically for healthcare data. This ensures your data is structured consistently and compatibly with best practices.
  4. Output: Creates well-defined tables (data marts) in your data warehouse, ready for analysis, reporting, and downstream use. These tables follow the Tuva Project's clinical data model structure.

🔌 Database Support​

  • Snowflake


Getting Started​

Step 1: Prerequisites​

Before you begin, ensure you have the following:

  1. Access to your data warehouse: Credentials and network access to your data warehouse instance (e.g. Snowflake, BigQuery).
  2. Cerner Data: Your raw Cerner data must be loaded into specific tables within your data warehouse.
  3. dbt CLI Installed: You need dbt (version 1.9 recommended) installed on your machine or environment where you'll run the transformations. See dbt Installation Guide for help with installation.
  4. Git: You need Git installed to clone this project repository.
  5. Authentication Details: These details will be important in connecting to dbt with a profiles.yml file.

Step 2: Clone the Repository​

Open your terminal or command prompt and clone this project:

git clone https://github.com/tuva-health/cerner_connector.git
cd cerner_connector

Step 3: Create and Activate Virtual Environment​

It's highly recommended to use a Python virtual environment to manage project dependencies. This isolates the project's packages from your global Python installation.

  1. Create the virtual environment (run this inside the cerner_connector directory):
# Use python3 if python defaults to Python 2
python -m venv venv

This creates a venv directory within your project folder.

  1. Activate the virtual environment:
  • macOS / Linux (bash/zsh): source venv/bin/activate
  • Windows (Command Prompt): venv\Scripts\activate.bat
  • Windows (PowerShell): .\venv\Scripts\Activate.ps1
  • Windows (Git Bash): source venv/Scripts/activate

You should see (venv) prepended to your command prompt, indicating the environment is active.

Step 4: Install Python Dependencies​

With the virtual environment active, install the required Python packages, including dbt and the warehouse-specific dbt adapter (e.g. dbt-snowflake, dbt-bigquery).

Step 5: Configure profiles.yml for Data Warehouse Connection​

dbt needs to know how to connect to your data warehouse. In general, this is done via a profiles.yml file, which you need to create. This file should NOT be committed to Git, as it contains sensitive credentials.

  • Location: By default, dbt looks for this file in ~/.dbt/profiles.yml (your user home directory, in a hidden .dbt folder).
  • Content: See the dbt docs.

Step 6: Install dbt Package Dependencies​

This project relies on external dbt packages (The Tuva Project and dbt_utils). Run the following command in your terminal from the project directory (the one containing dbt_project.yml):

dbt deps

This command reads packages.yml and downloads the necessary code into the dbt_packages/ directory within your project.

Step 7: Test the Connection​

Before running transformations, verify that dbt can connect to your data warehouse using your profiles.yml settings:

dbt debug

Look for "Connection test: OK connection ok". If you see errors, double-check your profiles.yml settings (account, user, role, warehouse, authentication details, paths).

Running the Project​

Once setup is complete, you can run the dbt transformations:

Full Run (Recommended First Time), this command will:

  • Run all models (.sql files in models/).
  • Run all tests (.yml, .sql files in tests/).
  • Materialize tables/views in your target data warehouse as configured.
dbt build

This might take some time depending on the data volume and warehouse size.

Run Only Models:​

If you only want to execute the transformations without running tests:

dbt run

Run Only Tests:​

To execute only the data quality tests:

dbt test

Running Specific Models:​

You can run specific parts of the project using dbt's node selection syntax. For example:

  • Run only the staging models: dbt run -s path:models/staging
  • Run a specific model and its upstream dependencies: dbt run -s +your_model_name