Frequently asked questions

The following lists a set of questions that we have been asked about Kedro in the past. If you have a different question which isn’t answered here, please consider asking it over on Stack Overflow.

What is Kedro?

Kedro is an open-source Python framework that applies software engineering best-practice to data and machine-learning pipelines. You can use it, for example, to optimise the process of taking a machine learning model into a production environment. You can use Kedro to organise a single user project running on a local environment, or collaborate in a team on an enterprise-level project.

For the source code, take a look at the Kedro repository on Github.

Kedro helps you build data pipelines that are robust, scaleable, deployable, reproducible and versioned. It was originally designed by Aris Valtazanos and Nikolaos Tsaousis at QuantumBlack to solve the challenges they faced in their project work.

This work was later turned into a product thanks to the following contributors:

Ivan Danov, Dmitrii Deriabin, Gordon Wrigley, Yetunde Dada, Nasef Khan, Kiyohito Kunii, Nikolaos Kaltsas, Meisam Emamjome, Peteris Erins, Lorena Balan, Richard Westenra and Anton Kirilenko.

What are the primary advantages of Kedro?

It is important to consider the primary advantages of Kedro over existing tools.

As we see it, Kedro emphasises a seamless transition from development to production without slowing the pace of the experimentation stage, because it:

  • Simplifies data access, using YAML configuration to define a single-source of truth for all data sources that your workflow requires
  • Uses a familiar data interface, by borrowing arguments from Pandas and Spark APIs meaning you do not have to learn a new API
  • Has a minimal pipeline syntax, that uses Python functions
  • Makes datasets 1st-level citizens, resolving task running order according to what each task produces and consumes, meaning you do not need to explicitly define dependencies between tasks
  • Has built-in runner selection, choosing sequential, parallel or thread runner functionality is a kedro run argument
  • Has a low-effort setup, that does not need a scheduler or database
  • Starts with a project template, which has built-in conventions and best practices from 140+ analytics engagements
  • Is flexible, simplifying your extension or replacement of core functionality e.g. the whole Data Catalog could be replaced with another mechanism for data access like Haxl

How does Kedro compare to other projects?

Data pipelines consist of extract-transform-load (ETL) workflows. If we understand that data pipelines must be scaleable, monitored, versioned, testable and modular then this introduces us to a spectrum of tools that can be used to construct such data pipelines. Pipeline abstraction is implemented in workflow schedulers like Luigi and Airflow, as well as in ETL frameworks like Bonobo ETL and Bubbles.

Kedro vs workflow schedulers

Kedro is not a workflow scheduler like Airflow and Luigi. Kedro makes it easy to prototype your data pipeline, while Airflow and Luigi are complementary frameworks that are great at managing deployment, scheduling, monitoring and alerting. A Kedro pipeline is like a machine that builds a car part. Airflow and Luigi tell the different Kedro machines to switch on or off in order to work together to produce a car. We have built a Kedro-Airflow plugin, providing faster prototyping time and reducing the barriers to entry associated with moving pipelines to Airflow.

Kedro vs other ETL frameworks

The primary differences to Bonobo ETL and Bubbles are related to the following features of Kedro:

  • Ability to support big data operations. Kedro supports big data operations by allowing you to use PySpark on your projects. We also look at processing dataframes differently to both tools as we consider entire dataframes and do not make use of the slower line-by-line data stream processing.
  • Project structure. Kedro provides a built-in project structure from the beginning of your project configured for best-practice project management.
  • Automatic dependency resolution for pipelines. The Pipeline module also maps out dependencies between nodes and displays the results of this in a sophisticated but easy to understand directed acyclic graph.

What is the philosophy behind Kedro?

Kedro is a Python library and lightly opinionated framework. This means that we give you the flexibility and extensibility of a standard Python library and make very few assumptions on the best way to do things. We have created independent but friendly modules – modules that understand each others’ defaults and are compatible. You can use alternative methods and choose to use one or all of the modules but it is understood that using Kedro in its entirety is the best thing that you can do for your projects.

The Kedro design principles are:

  • Declarative definitions
  • Composability
  • Flexibility
  • Extensibility
  • Simplicity

What is data engineering convention?

Bruce Philp and Guilherme Braccialli at QuantumBlack are the brains behind this model of managing data. To see which data layer to use, refer to the following table.

Note: The data layers don’t have to exist locally in the data folder within your project, but we recommend that you structure your S3 buckets or other data stores in a similar way.


Folder in data Description
Raw Initial start of the pipeline, containing the sourced data model(s) that should never be changed, it forms your single source of truth to work from. These data models are typically un-typed in most cases e.g. csv, but this will vary from case to case.
Intermediate Optional data model(s), which are introduced to type your raw data model(s), e.g. converting string based values into their current typed representation.
Primary Domain specific data model(s) containing cleansed, transformed and wrangled data from either raw or intermediate, which forms your layer that you input into your feature engineering.
Feature Analytics specific data model(s) containing a set of features defined against the primary data, which are grouped by feature area of analysis and stored against a common dimension.
Model input Analytics specific data model(s) containing all feature data against a common dimension and in the case of live projects against an analytics run date to ensure that you track the historical changes of the features over time.
Models Stored, serialised pre-trained machine learning models.
Model output Analytics specific data model(s) containing the results generated by the model based on the model input data.
Reporting Reporting data model(s) that are used to combine a set of primary, feature, model input and model output data used to drive the dashboard and the views constructed. It encapsulates and removes the need to define any blending or joining of data, improve performance and replacement of presentation layer without having to redefine the data models.

What version of Python does Kedro support?

Kedro is built for Python 3.6, 3.7 and 3.8.

How do I upgrade Kedro?

We use Semantic Versioning. The best way to safely upgrade is to check our release notes for any notable breaking changes. Follow the steps in the migration guide included for that specific release.

Once Kedro is installed, you can check your version as follows:

kedro --version

To later upgrade Kedro to a different version, simply run:

pip install kedro -U

When migrating an existing project to a newer Kedro version, make sure you also update the project_version in your ProjectContext, which is found in src/<package_name>/

How can I use a development version of Kedro?

Important: The development version of Kedro is not guaranteed to be bug-free and/or compatible with any of the stable versions. We do not recommend that you use a development version of Kedro in any production systems. Please install and use with caution.

If you want to try out the latest, most novel functionality of Kedro which has not been released yet, you can run the following installation command:

pip install git+

This will install Kedro from the develop branch of the GitHub repository, which is always the most up to date. This command will install Kedro from source, unlike pip install kedro which installs from PyPI.

If you want to rollback to the stable version of Kedro, execute the following in your environment:

pip uninstall kedro -y
pip install kedro

How can I find out more about Kedro?

There are a host of articles, podcasts, talks and Kedro showcase projects in the kedro-community repository.

Our preferred Kedro-community channel for feedback is through GitHub issues. We update the codebase regularly; you can find news about updates and features in the file on the Github repository.

How can I get my question answered?

If your question isn’t answered above, please consider asking it over on Stack Overflow or refer to the Kedro.Community Discourse channel that is managed by Kedroids all over the world.