Building a Historical Financial Data Lake at Bloomberg

Bloomberg’s Enterprise Data business has accumulated petabytes of historical financial data by taking point-in-time “snapshots” of financial entities and their attributes over four decades. Historical financial data is critical in backtesting models, evaluating risk, regulatory reporting, evaluating data quality, and more. Our Enterprise Data Lake engineering group ingested all historical text files (plus the ongoing snapshots that continue flowing in) into Apache Iceberg tables. This talk will include an overview of the challenges our organization needed to address, the open source architecture/tools we chose (Iceberg, Trino, etc.), and the impact this initiative has had on our business.

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