This case study explains how a global top-10 financial institution optimized analytics costs by implementing Denodo’s Embedded MPP engine. Previously, each department maintained separate Databricks clusters to query Delta tables in ADLS, leading to redundant compute and rising expenses. By centralizing analytical workloads on a shared Denodo MPP cluster while retaining Databricks for engineering and ML, the institution preserved governance and isolation while cutting compute waste. The shift identified up to $130,000 in annual savings and simplified cross-source analytics without sacrificing performance or compliance.

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