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Set of ETL utils for Spark
Reportable from driver?
and ensure -Dvar=value is used in the substitution
So that you can prune them out of the app.yaml
Ie. so that uri prefix is "parquet:" then we look for Parquet LoadWriter plugin, instantiate only that. Will cater for a combination of different strategies of reading/writing.
Might have a lifecycle:
which will be responsible for savings all transform results. It will need:
config section, eg:
sinker:
an abstract class:
spark_etl.parquet.Sink, extending the above
changes to MainUtil:
app.yaml should contain:
The pipelines are derived top-down, ie. see which "load" dependencies are defined in "transform", then which "transform" dependencies are defined in "extract"
Which will work inside Dataset.transform()
As demo implementation in "plugins" configuration section, which will eg. perform typing of Strings to Int/Double/Date/Timestamp/etc. Typing should be done Row[Strings] -> Row[Int/Double/Date/Timestamp], ie. not Row[String] -> case classes/classes.
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