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    Case Study

    Data Lakehouse Implementation

    Building a unified data lakehouse for a $17B asset manager — centralizing market feeds, alternative data, and research into a single queryable intelligence layer.

    Apache SparkDelta LakeAirflowAWS
    Lakehouse Pipeline — ProductionLive
    📡
    Data Ingestion
    Device telemetry, field reports, ERP feeds
    STREAMING
    Processing
    Apache Spark + Apache Trino
    BATCH + SQL
    💾
    Storage
    Delta Lake on AWS S3
    ACID
    🔄
    Orchestration
    Apache Airflow + Terraform (IaC)
    DAGs
    📊
    Visualization
    Looker + Python dashboards
    BI
    100+
    Live KPIs
    Weekly
    Auto Reports
    ML
    Predictions
    [01]

    The Challenge

    A $17B US-based asset manager was falling behind competitors who had already embraced data science. Their analysts spent 70% of their time wrangling spreadsheets from fragmented databases. Portfolio managers lacked visibility into model reliability, often relying on outdated forecasts. Internal IT faced complaints about data silos, while leadership was under pressure to show ROI from recent technology investments.

    The firm wanted to:

    • Consolidate all data into a single trusted repository.
    • Enable analysts to focus on insights instead of data prep.
    • Track model performance over time, ensuring transparency for CIOs and risk committees.
    [02]

    The Solution

    NSigma delivered a modern data lakehouse architecture optimized for financial data:

    Unified Ingestion

    Airbyte pipelines brought in fundamentals, alternative datasets, market feeds, and research exports into a single Snowflake lakehouse.

    Regression Tracking

    MLflow integration provided continuous monitoring of predictive models, logging version history, performance drift, and explainability metrics (e.g., SHAP values).

    Governance & Lineage

    Metadata tagging and automated audit trails gave compliance teams confidence.

    Visualization Layer

    Tailored dashboards allowed portfolio managers to see which models were contributing positively, under which market regimes.

    [03]

    The Results

    60%

    Analysts reclaimed their time, accelerating research throughput.

    +120 bps

    Alpha uplift in equity strategies over the first two quarters post-implementation.

    Audit-Ready

    CIOs gained transparency into every model decision.

    Scalable

    The system became the foundation for future ML projects with daily refreshes and automated retraining.

    [04]

    Why It Matters

    In modern asset management, alpha is as much about data infrastructure as it is about investment insight. This project showed how a properly designed lakehouse + governance system unlocks both productivity and performance, giving the firm a scalable edge against data-native competitors.

    Ready to modernize your data architecture?

    Let's discuss how a modern lakehouse can transform your analytics.

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