Comments (1)
In the example, transform_data
represents one step. In practice, there can be many such steps as you allude to. In this instance, best practice as far as I'm concerned, is to 'chain' the steps together - e.g.,
df_1 = cleaned_data(df)
df_2 = other_transformations(df_1)
...
What you do inside each transformation
function, and how you choose to do it (e.g. UDF functions or otherwise), is up to you. What we're trying to achieve here is ability to be able to compose ETL jobs from various sub-tasks and make those sub-tasks easy to test.
from pyspark-example-project.
Related Issues (18)
- PEX integration? HOT 1
- YAML instead of JSON HOT 1
- import sklearn fails HOT 1
- Nice work! Could you please add the license file? HOT 5
- Pyspark-best-practices
- `AttributeError: module 'logging' has no attribute 'Handler' in PySpark3
- Setup and Teardown should be @classmethods setUpClass and tearDownClass HOT 1
- etl_config.json not loaded in EMR HOT 1
- Wrong variables in example HOT 1
- Issue while executing the code via pycharm
- ModuleNotFoundError: No module named 'dependencies'
- Pass Parameters to Spark
- Add License HOT 1
- Failed TestCase
- Does not work when running on yarn in client mode HOT 1
- Having problems with running this code under PySpark 2.3.1 HOT 7
- Hard changes in structure of the project and documentation. HOT 4
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from pyspark-example-project.