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    Data Lakehouse Architecture

    Unify your data lake and warehouse into a single, high-performance platform for analytics and AI.

    Data Lakehouse Architecture

    The Challenge

    Organizations often maintain separate data lakes and data warehouses, leading to data silos, redundant storage costs, and inconsistent reporting. Data engineers spend significant time moving data between systems.

    Root Cause Analysis

    • Fragmented data estate: Raw data in lakes, curated data in warehouses — but no single source of truth
    • High latency: ETL pipelines between systems create hours or days of delay
    • Cost duplication: Storing the same data in multiple formats across multiple platforms
    • Governance gaps: Different security and access models across lake and warehouse

    How We Solve This with Cloud Technologies

    Unified Platform on Delta Lake / Apache Iceberg

    We design lakehouse architectures using open table formats (Delta Lake, Apache Iceberg) on cloud object storage (AWS S3, Azure ADLS, GCP Cloud Storage). This provides:

    • ACID transactions on data lake storage
    • Schema enforcement and evolution without pipeline rewrites
    • Time travel for auditing and rollback
    • Unified access for BI, ML, and streaming workloads

    Reference Architecture

    1. Ingestion Layer: Apache Kafka / Azure Event Hubs for real-time; Apache Airflow / Azure Data Factory for batch
    2. Storage Layer: Cloud object storage with Delta Lake / Iceberg table format
    3. Processing Layer: Apache Spark on Databricks / EMR for transformation
    4. Serving Layer: SQL endpoints for BI tools; Feature Store for ML models
    5. Governance Layer: Unity Catalog / Apache Atlas for lineage and access control

    Business Impact

    • 60% cost reduction by eliminating redundant warehouse storage
    • Near real-time analytics with streaming ingestion and incremental processing
    • Single governance model across all data assets