pipelinehub 0.1.22

A flexible data pipeline library for custom data processing workflows

PipelineHub

Python pipelines with automatic debugging built in.

pip install pipelinehub

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The problem

Your pipeline fails at 2am. The stack trace points to step 9. The actual problem happened in step 4 — a column silently changed dtype and nobody caught it until three steps later.

You spend the next four hours adding print statements, re-running on stale data, and guessing. I built this after doing exactly that, more times than I want to count.

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What it does

pipelinehub captures your data at every step — shape, nulls, dtypes, statistics — without any configuration. When something breaks, you see what the data looked like going into the failing step, not just which line exploded.

from pipelinehub import DataPipeline

pipeline = DataPipeline(name="ml-pipeline")
pipeline.add_step(clean_data, "clean")
pipeline.add_step(feature_engineer, "features")
pipeline.add_step(normalize, "normalize")

result = pipeline.execute(df)

When a step fails:

PipelineStepError: Step "normalize" (step 3 of 3) failed

Data entering this step:
  type:    dataframe
  shape:   (10420, 8)
  nulls:   col_price: 142
  dtypes:  col_id: object  col_price: float64

Original error: TypeError: unsupported operand type(s) for +: 'float' and 'str'

To replay from this step:
  pipeline.replay_from("normalize", your_data)

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Why not an AI assistant?

An AI assistant needs you to notice something is wrong, copy the data, describe the problem, and ask. pipelinehub runs at 3am on a schedule — no human in the loop. It catches silent failures that never throw an exception: null counts that creep up, dtypes that silently change, row counts that drop 60% in one step.

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Why not Kedro or Airflow?

Kedro requires a new project structure, a CLI, a data catalog, and YAML configs. Reasonable for a large team running production ML. Overkill if you're preprocessing data in a notebook or a script and just want visibility into what's happening at each step.

Airflow is an orchestrator. It schedules and monitors jobs — it doesn't help you understand what's wrong inside one.

pipelinehub is a library, not a framework. It adds one import and one method call to code you already have.

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Quick start

from pipelinehub import DataPipeline

pipeline = (DataPipeline(name="my-pipeline")
    .add_step(lambda x: [i for i in x if i > 0], "filter_positive")
    .add_step(lambda x: [i**2 for i in x], "square")
    .add_step(lambda x: [i/max(x) for i in x], "normalize"))

result = pipeline.execute([-2, -1, 0, 1, 2, 3, 4, 5])

Pass debug=False to skip snapshotting entirely — same behaviour as v0.1, no overhead:

result = pipeline.execute(data, debug=False)

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Features

• Automatic snapshots — shape, dtypes, null counts, and numeric stats captured at every step, zero configuration
• Rich failure context — PipelineStepError shows exactly what the data looked like entering the failing step
• Anomaly detection — warns when null counts spike, dtypes change, rows drop more than 50%, or value distributions shift compared to the last run
• Run history — every run stored locally in .pipelinehub/runs.db, no setup required
• Run comparison — pipeline.compare_runs() diffs any two runs step by step
• Replay from any step — re-run forward from any named step without re-running everything before it
• ph CLI — inspect run history, diff runs, and watch live execution from the terminal
• Fluent chaining — method chaining works if you prefer that style
• No external dependencies — stdlib only; pandas, polars, and numpy are detected and profiled if installed
• Full type hints — works with IDE autocomplete

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Snapshot output

When anomalies are detected, pipelinehub prints a summary after execute():

Pipeline completed  ✓  (2.3s)

  Step              Rows              Nulls          Duration
  ──────────────────────────────────────────────────────────
  clean             10500→10420       0→0            0.4s
  feature_engineer  10420→10420       0→142 ⚠        1.1s
  normalize         10420→10420       142→0          0.8s

⚠  col_price nulls introduced in step "feature_engineer" (+142)

Inspect run history

# Last run with all step snapshots
last = pipeline.last_run()

# Recent runs
pipeline.list_runs(limit=5)

# Compare last two runs
pipeline.compare_runs()

# Compare specific runs by ID
pipeline.compare_runs(run_id_a, run_id_b)

Replay from a step

# Fix normalize(), replay forward — skips clean and feature_engineer
result = pipeline.replay_from("normalize", your_data)

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ph CLI

pip install pipelinehub also installs the ph command. It reads from .pipelinehub/runs.db in your project — no auth, no setup.

# List recent runs
ph runs list

# Show last completed run
ph runs last

# Step-by-step detail for a specific run
ph runs show <run_id>

# Diff two runs
ph runs diff <run_id_a> <run_id_b>

# Watch a pipeline execute in real time (polls every 1s)
ph runs watch

# Health summary across all pipelines
ph status

# Aggregate stats — success rate, failure count
ph stats

# Check setup
ph doctor

Example output for ph runs list:

  ID         Pipeline               Started                Status       Steps
  ──────────────────────────────────────────────────────────────────────────
  a3f2c1b0   ml-pipeline            2026-06-24 09:12:03    ✅ success    4
  7d91e4a2   ml-pipeline            2026-06-23 22:47:11    ❌ failed     3
  c58b0f31   etl-daily              2026-06-23 06:00:02    ✅ success    6

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Examples

Data cleaning

from pipelinehub import DataPipeline

pipeline = (DataPipeline(name="cleaning")
    .add_step(lambda x: [float(i) for i in x if i is not None], "convert")
    .add_step(lambda x: [i for i in x if abs(i - sum(x)/len(x)) < 2.5], "remove_outliers")
    .add_step(lambda x: [(i - min(x)) / (max(x) - min(x)) for i in x], "normalize"))

result = pipeline.execute([1, 2, 3, None, 100, 4, 5, 6, 7, 8, 9])

Text processing

import re
from pipelinehub import DataPipeline

pipeline = (DataPipeline(name="text")
    .add_step(str.lower, "lowercase")
    .add_step(lambda t: re.sub(r'[^a-zA-Z0-9\s]', '', t), "clean")
    .add_step(str.split, "tokenize")
    .add_step(lambda words: sorted(set(w for w in words if len(w) >= 4)), "keywords"))

result = pipeline.execute("Hello World! This is a Sample Text for Processing...")

Pipeline management

pipeline = DataPipeline(name="example")
pipeline.add_step(lambda x: [i*2 for i in x], "double")
pipeline.add_step(lambda x: [i+1 for i in x], "add_one")

print(pipeline.get_steps())   # ['double', 'add_one']
print(len(pipeline))          # 2

pipeline.remove_step(0)
pipeline.clear_steps()

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Roadmap

v0.1 ✅ — Fluent pipeline chaining, zero dependencies, verbose mode
v0.2 ✅ — Automatic snapshot engine, rich failure context, run comparison, anomaly detection, replay, ph CLI
v0.3 — Web dashboard for run history and team visibility

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Contributing

git checkout -b feature/your-feature
pytest tests/
git commit -m 'Add your feature'
# open a pull request against main

Branch protection is on main — all changes go through a PR.

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License

MIT — see LICENSE for details.

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Built by Rahul Paul