walacor-data-tracker 0.0.2

SDK and CLI for capturing data-science lineage and persisting DAG snapshots to Walacor.

Walacor Data Tracking

---

A schema-first framework to track, version, and store the full lineage of data transformations — from raw ingestion to final model output — using Walacor as a backend snapshot store.

---

✨ Why this exists

• Reproducibility – Every transformation, parameter, and artifact is captured in a graph you can replay.
• Auditability – Snapshots are immutable, version-controlled, and timestamped.
• Collaboration – Team members see the same lineage and can compare or branch workflows.
• Extensibility – Strict JSON-schemas keep today’s pipelines clean while allowing tomorrow’s to evolve safely.

---

🏗️ Core Concepts

Concept	Stored as	Purpose
Transform Node	transform_node	One operation (e.g., “fit model”, “clean text”).
Transform Edge	transform_edge	Dependency between two nodes.
Project Metadata	project_metadata	Run-level info (owner, description, timestamps).

Immutable Snapshots
Once a DAG is written to Walacor, it cannot mutate—only a new snapshot (with a higher SV or run ID) can supersede it.

---

🚀 Getting Started

1. Install the SDKs

pip install walatrack 

Make sure you're using Python 3.10+ and have internet access to reach the Walacor API.

2. Initialize the Tracking Components

To begin capturing your data lineage:

• Start the Tracker – This manages the session and records operations.

• Attach an Adapter – For example, use PandasAdapter to automatically track DataFrame transformations.

• Add Writers – Choose where to send the output:

  • Console output for quick inspection
  • WalacorWriter to persist snapshots to the Walacor backend

Once set up, your transformation history will be automatically recorded and can be exported or persisted.

---

🧪 Example Use Cases

• Track changes in a machine learning pipeline
• Visualize column-level transformations in pandas
• Record versions of a dataset as it’s cleaned and merged
• Keep an auditable log of automated workflows

---

Here’s the updated README.md with a concise, illustrative example that highlights how easy it is to use walatrack. This is placed right after the Getting Started section and demonstrates a realistic tracking flow with minimal code:

---

🧪 Minimal Example

Here's how simple it is to start tracking transformations:

import pandas as pd
from walatrack import Tracker, PandasAdapter
from walatrack.writers import ConsoleWriter
from walatrack.writers.walacor import WalacorWriter

# 1. Start the tracker and adapter
tracker = Tracker().start()
adapter = PandasAdapter().start(tracker)

# 2. Define writers (console, or send to Walacor backend)
console_writer = ConsoleWriter()
walacor_writer = WalacorWriter(
    base_url="http://your-walacor-url/api",
    username="your-username",
    password="your-password",
    project_name="MyProject",
    description="Optiona Description"
)

# 3. Apply transformations as usual
df = pd.DataFrame({"id": [1, 2], "value": [100, 200]})
df2 = df.assign(new_val=df.value * 2)
df3 = df2.rename(columns={"value": "v"})

# 4. Stop and export the lineage
tracker.stop()

💡 The PandasAdapter automatically tracks operations like .assign(), .rename(), .merge(), etc., so you can work with pandas as usual — but with versioned lineage behind the scenes.

---

This snippet:

• Is short enough to understand at a glance
• Avoids hardcoded credentials or IPs
• Clearly reflects your existing setup
• Shows the power and simplicity of the library

🤝 Contributing

1. Fork → feature branch → PR.
2. Run pre-commit run --all-files.
3. Add/Update unit tests and schema definitions.
4. Keep the README & docs in sync.

---

📄 License

Apache 2.0 © 2025 Walacor & Contributors.