DataForge helps data analysts and engineers build and extend data solutions by leveraging modern software engineering principles.
DataForge enables writing of inline functions using single-column SQL expressions rather than CTEs, procedural scripts, or set-based models.
For an overview of the underlying concepts, check out this introduction blog.
Each function:
- is pure, with no side effects
- returns single column
- is composable with other functions
DataForge software engineering principles:
These principles allow DataForge projects to be easy to modify and extend - even with thousands of integrated pipelines.
Explore the Core CLI or learn more about how Core powers DataForge Cloud.
Dataforge Core is a code framework and command line tool to develop transformation functions and compile them into executable Spark SQL.
To run the CLI you will need:
- Java 8 or higher
- Amazon Corretto is a great option
- A PostgreSQL v14+ server with a dedicated empty database
- Check out our friends over at Tembo
- Python version 3.12+
The CLI also includes an integration to run the code in Databricks. To support this you need:
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Open a new command line window
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Validate Java and Python are installed correctly:
> java --version openjdk 21.0.3 2024-04-16 LTS> python --version Python 3.12.3 -
Install Dataforge by running:
> pip install dataforge-core Collecting dataforge-core... Installing collected packages: dataforge-core Successfully installed dataforge-core... -
Validate installation:
> dataforge --version dataforge-core 1.0.0 -
Configure connections and credentials to Postgres and optionally Databricks
> dataforge --configure Enter postgres connection string: postgresql://postgres:<postgres-server-url>:5432/postgres Do you want to configure Databricks SQL Warehouse connection (y/n)? y Enter Server hostname: <workspace-url>.cloud.databricks.com Enter HTTP path: /sql/1.0/warehouses/<warehouse-guid> Enter access token: <token-guid> Enter catalog name: <unity_catalog_name> Enter schema name: <schema_in_catalog_name> Connecting to Databricks SQL Warehouse <workspace-url>.cloud.databricks.com Databricks connection validated successfully Profile saved in C:\Users... -
Navigate to an empty folder and initialize project structure and sample files:
> dataforge --init Initialized project in C:\Users... -
Deploy dataforge structures to Postgres
> dataforge --seed All objects in schema(s) log,meta in postgres database will be deleted. Do you want to continue (y/n)? y Initializing database.. Database initialized -
Build sample project
> dataforge --build Validating project path C:\Users... Started import with id 1 Importing project files... <list of files> Files parsed Loading objects... Objects loaded Expressions validated Generated 8 source queries Generated 1 output queries Generated run.sql Import completed successfully -
Execute in Databricks
> dataforge --run Connecting to Databricks SQL Warehouse <workspace-url>.cloud.databricks.com Executing query Execution completed successfully
| -h, --help | Display this help message and exit |
| -v, --version | Display the installed DataForge version |
| -c, --configure | Connect to Postgres database and optionally Databricks SQL Warehouse |
| -s, --seed | Deploy tables and scripts to postgres database |
| -i, --init [Project Path] | Initialize project folder structure with sample code |
| -b, --build [Project Path] | Compile code, store results in Postgres, and generate target SQL files |
| -r, --run [Project Path] | Run compiled project on Databricks SQL Warehouse |
| -p, --profile [Profile Path] | Update path of stored credentials profile file |
