Visualise pipelines¶
Kedro-Viz displays data and machine-learning pipelines in an informative way, emphasising the connections between datasets and nodes. It shows the structure of your Kedro pipeline.
Use Kedro-Viz¶
This exercise assumes that you have been following the Spaceflights tutorial.
Visualise a whole pipeline¶
You should be in your project root directory, and once Kedro-Viz is installed you can visualise your pipeline by running:
kedro viz
This command will run a server on http://127.0.0.1:4141 that will open up your visualisation on a browser. You should be able to see the following:
Note: If a visualisation panel opens up and a pipeline is not visible then please check that your pipeline definition is complete. All other errors can be logged as GitHub Issues on the Kedro-Viz repository.
Exit an open visualisation¶
You exit this visualisation by closing the open browser and entering Ctrl+C or Cmd+C in your terminal.
Interact with Data Engineering Convention¶
The pipeline can be broken up into different layers to specify convention for how data is processed. This convention makes it easier to collaborate with other team members because everyone has an idea of what type of data cleaning or processing has happened. It also makes it easier to collaborate with yourself in future because it makes your data easier to reproduce if you have done something wrong. You can read more about the Data Engineering Convention that we use.
Kedro-Viz makes it easy to visualise these data processing stages by adding a layer
attribute to the datasets in the Data Catalog. We will be modifying catalog.yml
with the following:
companies:
type: pandas.CSVDataSet
filepath: data/01_raw/companies.csv
layer: raw
reviews:
type: pandas.CSVDataSet
filepath: data/01_raw/reviews.csv
layer: raw
shuttles:
type: pandas.ExcelDataSet
filepath: data/01_raw/shuttles.xlsx
layer: raw
preprocessed_companies:
type: pandas.CSVDataSet
filepath: data/02_intermediate/preprocessed_companies.csv
layer: intermediate
preprocessed_shuttles:
type: pandas.CSVDataSet
filepath: data/02_intermediate/preprocessed_shuttles.csv
layer: intermediate
master_table:
type: pandas.CSVDataSet
filepath: data/03_primary/master_table.csv
layer: primary
regressor:
type: pickle.PickleDataSet
filepath: data/06_models/regressor.pickle
versioned: true
layer: models
Run kedro-viz again with kedro viz
and observe how your visualisation has changed to accommodate the data processing stages as seen in the image below: