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🏢 Multi-Tenant Delta Lake Patterns

Status Complexity

Guide to implementing multi-tenant data architectures using Delta Lake.

🌟 Overview

Multi-tenant architectures enable serving multiple customers from shared infrastructure while maintaining data isolation and security.

🏗️ Pattern 1: Table per Tenant

# Create separate Delta tables for each tenant
def create_tenant_table(tenant_id, data_df):
    tenant_path = f"/delta/tenants/{tenant_id}/sales"
    data_df.write.format("delta").mode("overwrite").save(tenant_path)

🏗️ Pattern 2: Shared Table with Partitioning

# Single table partitioned by tenant_id
def write_multi_tenant_data(df, tenant_id):
    tenant_df = df.withColumn("tenant_id", lit(tenant_id))
    tenant_df.write.format("delta") \
        .mode("append") \
        .partitionBy("tenant_id", "date") \
        .save("/delta/multi_tenant/sales")

Last Updated: 2025-01-28 Documentation Status: Complete