db_zpark_pyf 0.1.0

A code-first approach to managing PySpark jobs, built on the pyfecto framework and geared for Databricks environments

db-zpark-pyf 🐍

A lightweight framework for writing modular, testable, and expressive data workflows in Python using PySpark and pyfecto.

This is the Python sibling of db-zpark, following similar principles for separation of concerns, functional programming, and structured workflow execution.

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✨ Key Concepts

• WorkflowTask: Top-level job orchestrator (e.g. for a pipeline or table group).
• WorkflowSubtask: A reusable unit of work representing a single table or logical step.
• WorkflowSubtasksRunner: A strategy to execute a collection of subtasks (e.g. sequentially).
• TaskEnvironment: Shared resources like SparkSession, passed to all components.
• Effect system: All execution is managed through PYIO (from pyfecto) for clean logging, retrying, and chaining.

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🚀 Example: Simulating a Delta Pipeline

Each table (users, orders, products) is handled by its own WorkflowSubtask, and all are coordinated by a WorkflowTask with a sequential runner.

🧪 Check the example here:
➡️ examples/delta_tables_workflow.py

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🔁 Databricks Runtime Compatibility

db-zpark-pyf	Pyfecto	Python	Spark	DBR
0.1.0	0.2.0	3.11	3.5.x	15.4 LTS

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🛠 Development Setup

📦 Install via pip

To use db-zpark-pyf in your own project:

pip install db_zpark_pyf