
Developed an Aggregated Data Quality (agg_dq) Demo Notebook for the Nike-Inc/spark-expectations repository, providing an end-to-end example for onboarding and validating data quality workflows. The notebook showcased how to set up Spark, define aggregation rules, and execute data quality expectations on sample datasets, incorporating interactive widgets for configuration and verification. This work leveraged Python and SQL within a Delta Lake environment, focusing on reproducibility by anchoring the demo to a specific commit. The resulting resource enabled teams to accelerate adoption of agg_dq features, offering a reusable, hands-on guide for both onboarding and ongoing validation of data quality processes.
September 2025 — Key deliverable: Aggregated Data Quality (agg_dq) Demo Notebook for spark-expectations. This end-to-end notebook demonstrates Spark setup, aggregation rule definitions, and execution of data quality expectations on sample data, with interactive widgets for configuration and verification. The work is anchored to commit a0a60f943de89518a94f793378ff33150f9de8bf, providing a reproducible reference for onboarding and validating agg_dq.
September 2025 — Key deliverable: Aggregated Data Quality (agg_dq) Demo Notebook for spark-expectations. This end-to-end notebook demonstrates Spark setup, aggregation rule definitions, and execution of data quality expectations on sample data, with interactive widgets for configuration and verification. The work is anchored to commit a0a60f943de89518a94f793378ff33150f9de8bf, providing a reproducible reference for onboarding and validating agg_dq.

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