Handling Missing Data in Distributed Systems: A Scala and GCP Dataproc Approach

To create a data pipeline on GCP Dataproc, we must load datasets from Google Cloud Storage, handle missing data, and perform data transformations.

Handling missing data effectively involves dropping unreliable rows, imputing missing numerical values, and assigning default values for categorical fields.

Data transformations should include aggregating purchase amounts by customer and joining customer information with transaction records on customer_id before saving the results.

Utilizing Spark's powerful data processing capabilities allows for efficient management of missing data and seamless integration of datasets in a unified pipeline.

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