dataforge-core 1.5.2

Command line compiler for dataforge core projects

DataForge helps data analysts and engineers build and extend data solutions by leveraging modern software engineering principles.

Understanding DataForge

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:

• Functional Programming
• Inversion of Control
• Single Responsibility Principal
• DRY

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.

Requirements

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+
  • Official Link

The CLI also includes an integration to run the code in Databricks. To support this you need:

• Databricks Workspace
• Databricks SQL Warehouse
• Developer Personal Access Token

Installation and Quickstart

• Open a new command line window

• 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
  

Commands

-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

Links

• https://dataforgelabs.com