mini-kedro

The mini-kedro Kedro starter

Introduction

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
example_dataset_1:
 type: pandas.CSVDataSet
 filepath: folder/filepath.csv

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

2. 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 = catalog.save("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.

Usage

Create a new project using this starter:

$ kedro new --starter=mini-kedro

Content

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 kedro-starter-pandas-iris starter
• An example notebook showing how to instantiate the DataCatalog and interact with the example dataset
• A README.md, which explains how to use the project created by the starter and how to transition to a full Kedro project afterwards