Scaling Data Engineering Pipelines: Preparing Credit Card Transactions Data for Machine Learning
Summary
Delta Lake's partitioning, z-ordering, and file compaction reduced Mastercard's 10+ petabyte transaction table file count by 70% and improved query performance by 80%. Databricks Workflows' foreach operator enabled parallel processing across multiple clusters, completing nine months of graph network computation in two days.
Summary generated by brickster.ai from the video transcript.
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