Al worked on the Shopify/discovery-apache-beam repository, delivering windowing support for the Dask runner in Apache Beam. By refactoring the Dask runner to apply windowing logic consistently across Dask operations, Al improved the correctness of windowed data handling, including side inputs and grouped data. This work enhanced compatibility with Beam’s windowing features, enabling more robust and reliable windowed data pipelines within the Shopify discovery stack. The implementation leveraged Python and Dask, focusing on distributed systems and data processing. Over the course of the month, Al’s contribution addressed a complex integration challenge, demonstrating depth in distributed data engineering and system reliability.