Here we show how one can define some transformation logic that uses the new Apache Hamilton data quality feature with Pandera In addition, we also show how you can manage execution on top of Ray, Pandas on Spark.

This example is ALMOST IDENTICAL to examples/data_quality/simple except that it instead uses Pandera as input to @check_output, and has a different feature logic file for Spark. Pandera supports all "dataframe" like data types, so works across Pandas, Dask, and Pandas on Spark.

We want to create some features for input to training a model. The task is to try to predict absenteeism at work. Inspiration comes from here and here.

There are three parts to this task.

1. Write data loading logic.
2. Write feature transform logic.
3. Writing logic to materialize a dataframe from the above two parts.

These three parts map to the files laid out below -- with part (3) having multiple possible files.

We will be adding @check_output decorators to step (2). You could add them to step (1) if you like, but we omit that here as an exercise for the reader.

• Absenteeism_at_work.csv - the raw data set. Source.
• data_loaders.py - a module that says how the data should be loaded. If we want to use native data types (e.g. dask data types) this is where we would make that happen.
• feature_logic.py - this module contains some feature transformation code. It is annotated with @check_output. The same codes should happily run on top of Dask, and Ray.
• feature_logic_spark.py - this module contains some feature transformation code specific to running on top of Pandas on Spark. Specifically, note that the data types checked against are different than in feature_logic.py.
• run.py - this is the default Apache Hamilton way of materializing features.
• run_dask.py - this shows how one would materialize features using Dask.
• run_ray.py - this shows how one would materialize features using Ray.
• run_spark.py - this shows how one would materialize features using Pandas on Spark.

Each file should have some documentation at the top to help identify what it is and how you can use it.

Running the code involves installing the right python dependencies, and then executing the code. It is best practice to create a python virtual environment for each project/example; we omit showing that step here.

The DAG created for each way of executing is logically the same, though it might involve different functions being executed in the case of spark and dask; the @config.when* decorators are used for this purpose.

Note: importance is not specified in the @check_outputdecorators found in feature_logic.py. The default behavior is therefore invoked, which is to log a "warning" and not stop execution. If stopping execution is desired, importance="fail" should then be added to the decorators; more centralized control is going to be added in future releases.

pip install -r requirements.txt python run.py

It is best practice to create a python virtual environment for each project/example. We omit showing that step here.

pip install -r requirements-dask.txt python run_dask.py

It is best practice to create a python virtual environment for each project/example. We omit showing that step here.

pip install -r requirements-ray.txt python run_ray.py

It is best practice to create a python virtual environment for each project/example. We omit showing that step here.

pip install -r requirements-spark.txt python run_spark.py

Again you'll see that the visualizations don't change much between the different ways of executing. But to help you visualize what's going on, here is the output of visualize_execution for each of them.