Modern data lakes are increasingly multi-tenant, serving multiple teams, departments, or clients from a shared platform. These tenants often have unique data isolation, access control, and schema requirements. To support such complexity, data lake engines must offer scalable ingestion, transactional guarantees, and flexible query access.

Apache Hudi has emerged as a key component in multi-tenant lakehouse architectures. With its support for ACID transactions, schema evolution, incremental processing, and time-travel, Hudi empowers organizations to build secure, governed, and real-time multi-tenant data lakes.

In this blog, we explore how Hudi supports multi-tenancy, what benefits it provides, and best practices for designing tenant-aware pipelines and storage strategies.

What Is a Multi-Tenant Data Lake?

A multi-tenant data lake allows different tenants (teams, apps, clients) to:

  • Ingest and access data independently
  • Maintain schema isolation
  • Share the same underlying infrastructure
  • Enforce role-based access and audit controls

This setup enables cost efficiency, governance, and collaborative analytics without deploying separate clusters for each tenant.

How Hudi Supports Multi-Tenancy

Apache Hudi offers several features that align with multi-tenant design patterns:

  1. Namespace Isolation via Partitioning or Table Paths
  2. ACID Transactions for Safe Concurrent Writes
  3. Incremental Views for Tenant-Specific Pipelines
  4. Schema Evolution and Compatibility
  5. Metadata Table for Scalable File Listing
  6. Time Travel for Auditability and Rollback

Tenant Isolation Patterns in Hudi

There are multiple ways to isolate tenant data in Hudi:

1. Table-Level Isolation Each tenant has a separate Hudi table:

/data/hudi/tenant_a/events
/data/hudi/tenant_b/events

Pros:

  • Clean separation
  • Easy access control using storage-level policies

Cons:

  • Metadata fragmentation
  • Harder to optimize across tenants

2. Partition-Level Isolation Use a tenant_id or org_id field as the partition key:

df.write.format("hudi")
.option("hoodie.datasource.write.partitionpath.field", "tenant_id")
.save("/data/hudi/multi_tenant_events")

Pros:

  • Better storage utilization
  • Single metadata index

Cons:

  • More complex access control and compaction management

3. Column-Based Filtering Store all data in one table and enforce row-level filters using access control tools like Ranger or lakehouse query engines.

Schema Evolution Per Tenant

Hudi supports schema-on-write with evolution capabilities, allowing:

  • Addition of new columns
  • Safe handling of optional/nullable fields
  • Backward and forward compatibility (if configured)

This is essential in multi-tenant environments where schemas evolve independently.

To enable schema evolution:

hoodie.avro.schema.validate=false
hoodie.avro.schema.allow.auto.evolution=true

Incremental Query Support

Tenants may require isolated views of newly ingested data.

Use Hudi’s incremental pull to stream updates per tenant:

df = spark.read.format("hudi")
.option("hoodie.datasource.query.type", "incremental")
.option("hoodie.datasource.read.begin.instanttime", "20240416120000")
.load("/data/hudi/multi_tenant_events")

tenant_data = df.filter("tenant_id = 'tenant_b'")

This enables tenant-specific pipelines that avoid scanning historical data.

Compaction and Metadata Scaling

Multi-tenant setups often lead to:

  • Many small files (per partition/tenant)
  • High compaction overhead
  • Metadata growth

Recommendations:

  • Enable the metadata table: 
    hoodie.metadata.enable=true
  • Use asynchronous compaction for Merge-on-Read: 
    hoodie.compact.inline=false
  • Run partition-aware clustering to group small files by tenant

Access Control and Governance

Hudi integrates with:

  • AWS Lake Formation
  • Apache Ranger
  • Unity Catalog (Databricks)

Use these tools to:

  • Enforce row-level security by tenant_id
  • Restrict access to specific table paths
  • Monitor tenant-specific usage for billing or auditing

Best Practices for Multi-Tenant Hudi Deployments

  • Design tenant-aware record keys: 
    tenant_id:event_id
  • Use partition pruning for efficient reads
  • Apply per-tenant retention policies
  • Enable Hive/Glue sync for metadata consistency
  • Monitor write performance per tenant
  • Consider Z-ordering or clustering for multi-dimensional tenant access

Conclusion

Apache Hudi provides powerful capabilities that make it a natural fit for multi-tenant data lakes. With native support for transactional ingestion, schema evolution, and incremental processing, Hudi ensures each tenant’s data remains isolated, queryable, and governed.

By applying the right partitioning, schema, and access control strategies, organizations can deliver scalable and secure lakehouse experiences — all on top of a shared Hudi-powered architecture.