A Guide to Kedro: Your Production-Ready Data Science Toolbox

2026-03-04 · Source: KDnuggets · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, medium

Summary

Kedro, an open-source framework by QuantumBlack, helps bridge the gap between experimental data science notebooks and production-ready solutions by enforcing project structure, scalability, and reproducibility. This guide introduces Kedro's core features, starting with installation via `pip install kedro` and project creation using `kedro new`. It details setting up a data catalog in `conf/base/catalog.yml` to define datasets like `raw_customers` (pandas.CSVDataset) and `processed_features` (pandas.ParquetDataset), isolating data definitions from code. The article then walks through creating a data processing pipeline with `kedro pipeline create data_processing`, defining modular functions (nodes) for feature engineering and data splitting in `nodes.py`. These nodes are then integrated into `pipeline.py`, referencing datasets by name. Finally, it covers configuring external parameters in `conf/base/parameters.yml` (e.g., `test_fraction: 0.2`), disabling PySpark imports in `settings.py`, and executing the pipeline with `kedro run`, with an optional visualization using `kedro-viz`.

Key takeaway

For Data Scientists and ML Engineers looking to transition exploratory notebook work into robust, production-grade systems, adopting Kedro can significantly improve project structure and maintainability. You should consider integrating Kedro's modular pipelines and explicit data cataloging into your workflow to enhance reproducibility and collaboration. This approach helps standardize data handling and processing, making your projects easier to scale and deploy.

Key insights

Kedro structures data science projects for production readiness through modular pipelines and explicit data cataloging.

Principles

• Isolate data definitions from code.
• Modularize data science workflows into nodes.
• Externalize configuration parameters.

Method

Define datasets in `catalog.yml`, create Python functions as pipeline nodes, link nodes and datasets in `pipeline.py`, and configure parameters in `parameters.yml`.

In practice

• Use `kedro new` for structured project setup.
• Define data sources in `catalog.yml`.
• Visualize pipelines with `kedro viz`.

Topics

• Kedro
• Data Pipelines
• MLOps
• Data Catalog
• Feature Engineering

Code references

• kedro-org/kedro
• gakudo-ai/open-datasets

Best for: Data Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.