findrum-platform 0.2.1

Framework for building modular, event-driven data pipelines

FinDrum

FinDrum is a lightweight Python framework for building and orchestrating data pipelines with extensible architecture via operators, datasources, schedulers, and triggers.

This repository (FinDrum-Platform) is the core package and is meant to be used as a library. Custom logic (pipelines and extensions) should be defined in external projects.

---

Installation

pip install findrum-platform

---

Overview

Findrum pipelines are defined in YAML files and can include:

• A sequence of operators
• A datasource (provides data from external source)
• A scheduler (to run periodically)
• An event trigger (to respond to changes)

Example structure:

scheduler:
  type: MyCustomScheduler

pipeline:
  - id: step1
    datasource: MyDataSource
    params:
      key: value
  - id: step2
    operator: MyOperator
    depends_on: step1
    params:
      key: value

---

Interfaces

Findrum provides a minimal interface for each pipeline component. These are abstract base classes that must be subclassed by your custom logic.

Operator – Core processing unit

from findrum.interfaces import Operator

class MyOperator(Operator):
    def run(self, input_data):
        ...

Use when defining a step in a pipeline. Must implement run(input_data). We recommend that it returns a pandas.DataFrame.

---

DataSource – Step that starts a pipeline

from findrum.interfaces import DataSource

class MySource(DataSource):
    def fetch(self, **kwargs):
        ...

We recommend that it returns a pandas.DataFrame. It feeds the pipeline with data.

---

Scheduler – Periodic trigger for pipelines

from findrum.interfaces import Scheduler

class MyScheduler(Scheduler):
    def register(self, scheduler):
        # e.g., add job to APScheduler instance
        ...

Implements logic to execute the pipeline on a time interval or schedule.

---

EventTrigger – React to system/file/bucket events

from findrum.interfaces import EventTrigger

class MyTrigger(EventTrigger):
    def start(self):
        # Starts a file watcher, webhook listener, etc.
        ...

Runs the pipeline when a specific external event happens.

---

Core Classes

You can import and use the main classes provided by Findrum:

from findrum import Platform

• Platform: Main entrypoint to manage pipelines, register them, and run based on schedule or events.

---

CLI Usage: findrum-run

After installing findrum-platform, a CLI tool is available:

Run a pipeline immediately

findrum-run pipelines/my_pipeline.yaml

Use a custom config file for extensions

findrum-run pipelines/my_pipeline.yaml --config config/config.yaml

Enable logging (INFO level)

findrum-run pipelines/my_pipeline.yaml --verbose

---

Extension Discovery

Findrum requires a config.yaml file with registered class paths:

operators:
  - my_project.operators.MyCustomOperator

datasources:
  - my_project.datasources.MyDataSource

schedulers:
  - my_project.schedulers.MyScheduler

triggers:
  - my_project.triggers.MyTrigger

This lets Findrum dynamically import your components.

---

Minimal Example For a Non-CLI runner

from findrum import Platform

platform = Platform("config.yaml")
platform.register_pipeline("pipelines/my_pipeline.yaml")
platform.start()

You can also run your pipelines from a python file (like main.py for example) following the example above.

---

Clean Project Structure

A typical project using Findrum should look like:

your-project/
├── operators/
│   └── my_operator.py
├── schedulers/
│   └── my_scheduler.py
├── triggers/
│   └── my_trigger.py
├── datasources/
│   └── my_datasource.py
├── pipelines/
│   └── my_pipeline.yaml
├── config.yaml
└── main.py (optional)

Getting Started With Examples

To get started quickly, FinDrum includes runnable examples in the examples/ folder.