Lakehouse vs Warehouse in Fabric: Making the Right Choice

One of the most common questions in Fabric is when to use Lakehouse vs Warehouse. Let’s break down the decision factors.

Feature Comparison

from dataclasses import dataclass
from typing import List, Dict

@dataclass
class FeatureComparison:
    feature: str
    lakehouse: str
    warehouse: str
    winner: str
    notes: str

comparisons = [
    FeatureComparison(
        feature="Query Language",
        lakehouse="Spark SQL, PySpark, T-SQL (via SQL endpoint)",
        warehouse="T-SQL (full support)",
        winner="Warehouse for T-SQL, Lakehouse for Spark",
        notes="Warehouse supports stored procedures, functions"
    ),
    FeatureComparison(
        feature="Data Format",
        lakehouse="Delta Lake (Parquet-based)",
        warehouse="Proprietary (optimized for SQL)",
        winner="Lakehouse for open format",
        notes="Lakehouse data accessible outside Fabric"
    ),
    FeatureComparison(
        feature="Data Engineering",
        lakehouse="Native Spark support",
        warehouse="Limited (use T-SQL or call notebooks)",
        winner="Lakehouse",
        notes="Lakehouse is designed for Spark workloads"
    ),
    FeatureComparison(
        feature="Complex SQL",
        lakehouse="Basic via SQL endpoint",
        warehouse="Full T-SQL support",
        winner="Warehouse",
        notes="Stored procs, CTEs, window functions fully supported"
    ),
    FeatureComparison(
        feature="Power BI Integration",
        lakehouse="Direct Lake mode",
        warehouse="Direct Query or Import",
        winner="Lakehouse for large datasets",
        notes="Direct Lake provides import-like speed without copying data"
    ),
    FeatureComparison(
        feature="Schema Evolution",
        lakehouse="Flexible (schema on read option)",
        warehouse="Strict (schema on write)",
        winner="Lakehouse for flexibility",
        notes="Lakehouse supports mergeSchema"
    ),
    FeatureComparison(
        feature="ACID Transactions",
        lakehouse="Yes (Delta Lake)",
        warehouse="Yes (native)",
        winner="Tie",
        notes="Both support transactions"
    ),
    FeatureComparison(
        feature="Time Travel",
        lakehouse="Yes (Delta Lake versioning)",
        warehouse="Limited (no native support)",
        winner="Lakehouse",
        notes="Lakehouse can query historical versions"
    ),
    FeatureComparison(
        feature="Semi-structured Data",
        lakehouse="Native support (JSON, XML, etc.)",
        warehouse="Limited (JSON functions)",
        winner="Lakehouse",
        notes="Lakehouse handles nested data natively"
    ),
    FeatureComparison(
        feature="ML Workloads",
        lakehouse="Native (Spark MLlib, integrations)",
        warehouse="Via export or external tools",
        winner="Lakehouse",
        notes="Lakehouse is preferred for ML pipelines"
    )
]

def generate_comparison_table():
    """Generate markdown comparison table."""
    table = "| Feature | Lakehouse | Warehouse | Better For |\n"
    table += "|---------|-----------|-----------|------------|\n"

    for c in comparisons:
        table += f"| {c.feature} | {c.lakehouse[:30]}... | {c.warehouse[:30]}... | {c.winner} |\n"

    return table

Decision Framework

class LakehouseWarehouseDecider:
    """Help decide between Lakehouse and Warehouse."""

    def __init__(self):
        self.questions = [
            {
                "id": "primary_language",
                "question": "What's your primary query/transformation language?",
                "options": {
                    "spark": {"lakehouse": 3, "warehouse": 0},
                    "tsql": {"lakehouse": 1, "warehouse": 3},
                    "both": {"lakehouse": 2, "warehouse": 2}
                }
            },
            {
                "id": "stored_procedures",
                "question": "Do you need stored procedures and T-SQL functions?",
                "options": {
                    "yes_critical": {"lakehouse": 0, "warehouse": 3},
                    "yes_nice_to_have": {"lakehouse": 1, "warehouse": 2},
                    "no": {"lakehouse": 2, "warehouse": 1}
                }
            },
            {
                "id": "data_engineering",
                "question": "How much data engineering/ETL do you do?",
                "options": {
                    "heavy_spark": {"lakehouse": 3, "warehouse": 0},
                    "moderate": {"lakehouse": 2, "warehouse": 1},
                    "light_sql": {"lakehouse": 1, "warehouse": 2}
                }
            },
            {
                "id": "data_volume",
                "question": "What's your data volume?",
                "options": {
                    "very_large_tb": {"lakehouse": 3, "warehouse": 1},
                    "large_hundreds_gb": {"lakehouse": 2, "warehouse": 2},
                    "small_tens_gb": {"lakehouse": 1, "warehouse": 2}
                }
            },
            {
                "id": "bi_requirements",
                "question": "What are your Power BI requirements?",
                "options": {
                    "large_datasets_fast": {"lakehouse": 3, "warehouse": 1},
                    "complex_dax": {"lakehouse": 2, "warehouse": 2},
                    "simple_reports": {"lakehouse": 2, "warehouse": 2}
                }
            },
            {
                "id": "ml_requirements",
                "question": "Do you have ML/data science requirements?",
                "options": {
                    "yes_heavy": {"lakehouse": 3, "warehouse": 0},
                    "yes_some": {"lakehouse": 2, "warehouse": 1},
                    "no": {"lakehouse": 1, "warehouse": 2}
                }
            },
            {
                "id": "open_format",
                "question": "Is open data format important?",
                "options": {
                    "critical": {"lakehouse": 3, "warehouse": 0},
                    "nice_to_have": {"lakehouse": 2, "warehouse": 1},
                    "not_important": {"lakehouse": 1, "warehouse": 2}
                }
            }
        ]

    def evaluate(self, answers: Dict[str, str]) -> Dict:
        """Evaluate answers and provide recommendation."""
        lakehouse_score = 0
        warehouse_score = 0

        for question in self.questions:
            answer = answers.get(question["id"])
            if answer and answer in question["options"]:
                scores = question["options"][answer]
                lakehouse_score += scores["lakehouse"]
                warehouse_score += scores["warehouse"]

        total = lakehouse_score + warehouse_score
        lakehouse_pct = (lakehouse_score / total * 100) if total > 0 else 50
        warehouse_pct = (warehouse_score / total * 100) if total > 0 else 50

        if lakehouse_pct > 60:
            recommendation = "Lakehouse"
            reasoning = "Your requirements align strongly with Lakehouse capabilities"
        elif warehouse_pct > 60:
            recommendation = "Warehouse"
            reasoning = "Your requirements align strongly with Warehouse capabilities"
        else:
            recommendation = "Hybrid (Both)"
            reasoning = "Consider using both: Lakehouse for ingestion/transformation, Warehouse for analytics"

        return {
            "recommendation": recommendation,
            "lakehouse_score": lakehouse_score,
            "warehouse_score": warehouse_score,
            "lakehouse_percentage": lakehouse_pct,
            "warehouse_percentage": warehouse_pct,
            "reasoning": reasoning
        }

# Example usage
decider = LakehouseWarehouseDecider()
result = decider.evaluate({
    "primary_language": "spark",
    "stored_procedures": "no",
    "data_engineering": "heavy_spark",
    "data_volume": "very_large_tb",
    "bi_requirements": "large_datasets_fast",
    "ml_requirements": "yes_some",
    "open_format": "critical"
})
print(f"Recommendation: {result['recommendation']}")

Common Patterns

architecture_patterns = {
    "lakehouse_only": {
        "description": "Pure Lakehouse architecture",
        "when_to_use": [
            "Heavy Spark-based data engineering",
            "ML/data science workloads",
            "Large datasets with Direct Lake",
            "Open format requirements"
        ],
        "data_flow": """
Sources -> Bronze (Lakehouse) -> Silver (Lakehouse) -> Gold (Lakehouse) -> Direct Lake -> Power BI
        """,
        "example_code": """
# All layers in Lakehouse
spark.table("bronze.raw_data")
    .transform(clean_data)
    .write.format("delta").saveAsTable("silver.clean_data")

spark.table("silver.clean_data")
    .transform(aggregate_metrics)
    .write.format("delta").saveAsTable("gold.metrics")
"""
    },
    "warehouse_only": {
        "description": "Pure Warehouse architecture",
        "when_to_use": [
            "T-SQL centric organization",
            "Complex stored procedures",
            "Migration from traditional DW",
            "Strong SQL skills, limited Spark"
        ],
        "data_flow": """
Sources -> Staging (Warehouse) -> Cleansed (Warehouse) -> Marts (Warehouse) -> Power BI
        """,
        "example_code": """
-- T-SQL ETL
EXEC dbo.usp_LoadStagingData;
EXEC dbo.usp_TransformToClean;
EXEC dbo.usp_BuildMarts;
"""
    },
    "hybrid_lakehouse_warehouse": {
        "description": "Lakehouse for engineering, Warehouse for analytics",
        "when_to_use": [
            "Mixed skill sets",
            "Complex analytical queries",
            "Need for stored procedures AND Spark",
            "Best of both worlds"
        ],
        "data_flow": """
Sources -> Bronze (Lakehouse) -> Silver (Lakehouse) -> Gold (Warehouse) -> Power BI
        """,
        "example_code": """
# Lakehouse for bronze/silver
spark.table("bronze.raw").transform(clean).saveAsTable("silver.clean")

-- Warehouse for gold (via shortcut or copy)
CREATE TABLE gold.fact_sales AS
SELECT ... FROM silver.clean_sales;

-- Complex analytics in Warehouse
CREATE PROCEDURE dbo.CalculateMetrics AS ...
"""
    },
    "lakehouse_with_sql_endpoint": {
        "description": "Lakehouse accessed via SQL",
        "when_to_use": [
            "Team comfortable with SQL",
            "Don't need stored procedures",
            "Want Lakehouse benefits with SQL interface",
            "Gradual Spark adoption"
        ],
        "data_flow": """
Sources -> Lakehouse (all layers) -> SQL Endpoint -> Power BI / SQL tools
        """,
        "example_code": """
-- Access Lakehouse via SQL endpoint
SELECT * FROM lh_sales.dbo.fact_transactions
WHERE transaction_date >= '2023-01-01';
"""
    }
}

def recommend_pattern(requirements: Dict) -> str:
    """Recommend architecture pattern."""
    if requirements.get("spark_only"):
        return "lakehouse_only"
    elif requirements.get("tsql_only"):
        return "warehouse_only"
    elif requirements.get("mixed_skills"):
        return "hybrid_lakehouse_warehouse"
    else:
        return "lakehouse_with_sql_endpoint"

Tomorrow, we’ll explore when to use what in Fabric!