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In python data science/engineering most of our data is in the form of some sort of table, typically a DataFrame from a library like pandas, spark, or dask.
DataFrames are the heart of most pipelines
These containers for data contain many convenient methods to manipulate table like data structures. Sometimes we leverage other data types, namely vanilla types like lists and dicts, or even numpy data types.
unfamiliar with kedro, check out this post
Sometimes datasets are not tables
There are times when our data doesn't fit nicely into a DataFrame. Lucky for us Kedro has pickle support out of the box. Pickle is a way to store any python object to disk. Beware that pickle files coming from an unknown source can run malicous code and are considered unsafe. For the most part though when you read and write your own pickle files they are a good tool to consider.
See more about pickle from python.org.
Cataloging Pickle
I may have a dictionary that describes some cars.
{ 'truck-012-abc': { 'type': 'truck' 'sales': [12, 2, 3, 4, 8] 'weight': 9024, 'accesories': ['leather', 'audio-1'] }
In the catalog we will simply set the type as pickle.PickleDataSet and give
it a filepath.
cars: filepath: data/cars.pkl type: pickle.PickleDataSet
This filepath does not have to be on the local filesystem it can be on the cloud thanks to how kedro utilizes fsspec for each of its datasets.
Loading the dataset
The benefit of cataloging this dataset compared to leaving it as a
MemoryDataSet is that you can easily load this data back into memory for
further development or debugging without running any of the pipeline.
catalog.load('cars')