The mini-kedro Kedro starter


Mini-Kedro makes it possible to use the DataCatalog functionality in Kedro. Use Kedro to configure and explore data sources in a Jupyter notebook using the DataCatalog feature.

The DataCatalog allows you to specify data sources that you interact with for loading and saving purposes using a YAML API. See an example:

# conf/base/catalog.yml
 type: pandas.CSVDataSet
 filepath: folder/filepath.csv

 type: spark.SparkDataSet
 filepath: s3a://your_bucket/data/01_raw/example_dataset_2*
 credentials: dev_s3
 file_format: csv
   if_exists: replace

Later on, you can build a full pipeline using the same configuration, with the following steps:

  1. Create a new empty Kedro project in a new directory

kedro new

Let’s assume that the new project is created at /path/to/your/project.

  1. Copy the conf/ and data/ directories over to the new project

cp -fR {conf,data} `/path/to/your/project`

This makes it possible to use something like df = catalog.load("example_dataset_1") and df_2 ="example_dataset_2") to interact with data in a Jupyter notebook. The advantage of this approach is that you never need to specify file paths for loading or saving data in your Jupyter notebook.


Create a new project using this starter:

$ kedro new --starter=mini-kedro


The starter contains:

  • A conf/ directory, which contains an example DataCatalog configuration (catalog.yml)

  • A data/ directory, which contains an example dataset identical to the one used by the pandas-iris starter

  • An example notebook showing how to instantiate the DataCatalog and interact with the example dataset

  • A, which explains how to use the project created by the starter and how to transition to a full Kedro project afterwards