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Interactive 3D/Data Warehouse vs Lake vs Lakehouse
Data Architecture Evolution
Data Warehouse → Data Lake → Lakehouse
Data Warehouse
Source Systems
ETL / Transform
Columnar Storage (proprietary)
BI / SQL Queries
Data Lake
Source Systems
Raw Ingestion (no schema)
Object Storage (S3/GCS)
Spark / Hive / Presto
Lakehouse
Source Systems
Streaming + Batch Ingest
Open Format (Parquet + metadata layer)
SQL + ML + Streaming unified
Lakehouse - Detail
Data Flow
Source → unified pipeline → open storage → SQL / Spark / ML / streaming all read same data
Strength
Best of both worlds. ACID + cheap storage + ML-ready. No training-serving skew. Open formats.
Weakness
More complex than either warehouse or lake alone. Requires understanding of metadata layer.
For Use Case: ML Training
Ideal: point-in-time correct reads prevent label leakage. Unified offline + online path.
Feature Comparison Matrix
FeatureWarehouseLakeLakehouse
ACID✓✗✓
Schema Enforcement✓✗✓
Streaming✗⚠ complex✓
ML Workloads⚠ limited✓✓
Cost$$$$$$$
Query PerformanceExcellentSlowGood
Cost: Warehouse ($$$$ - Snowflake/Redshift) > Lakehouse ($$ - Databricks/Iceberg on S3) > Lake ($ - raw S3/GCS)
Architecture
Open Format
Best Spark integration. Used by Databricks platform. Strong time-travel.
Use Case
Display

Data Warehouse vs Lake vs Lakehouse - Interactive Visualization

The data lakehouse combines the best properties of data warehouses (ACID transactions, schema enforcement, fast SQL) with data lakes (cheap object storage, raw data, ML workloads). Powered by open table formats like Delta Lake (Databricks), Apache Iceberg (Netflix), and Apache Hudi (Uber), the lakehouse enables unified ML training, analytics, and streaming on the same data. It eliminates the training-serving skew caused by separate warehouse and lake pipelines, and supports point-in-time correct reads for ML feature engineering.

  • Data warehouse: fast SQL, ACID, proprietary format - cannot scale to petabyte ML training workloads
  • Data lake: cheap S3 storage, any format - but no schema enforcement leads to data swamp without governance
  • Lakehouse: ACID + open format (Parquet + metadata layer) - Delta Lake, Iceberg, and Hudi all solve this
  • ML advantage: point-in-time correct reads on the lakehouse prevent label leakage in training datasets

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