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Apache Kudu vs Apache Paimon

Apache Kudu: Columnar storage engine for fast analytics on fast-changing data, paired with Impala or Spark. Apache Paimon: Streaming lakehouse table format with LSM-based upserts and changelogs, born in Apache Flink.

key differences

  • Apache Paimon is built for larger datasets (Multi-PB) than Apache Kudu (≤ 1 PB).
  • Transactions: Apache Kudu offers limited ACID; Apache Paimon offers full.
  • Only Apache Kudu has encryption at rest, role-based access, online schema changes.
  • Only Apache Paimon has transactions, change data capture, time travel, branching, streaming ingest.
  • Apache Paimon can run embedded in your application process.

choose apache-kudu for

  • +Mutable fact tables in Cloudera
  • +Near-real-time analytics with Impala
  • +Time-series with updates

best at · Real-time analytics, OLAP, Time-series

choose apache-paimon for

  • +Streaming CDC from databases into the lake
  • +Real-time lakehouse with Flink
  • +Incremental ETL with changelogs
  • +Partial-update wide tables

best at · Lakehouse, Streaming, Real-time analytics

overview
Primary model
Columnar / OLAP
Columnar / OLAP
Also supports
Relational
Relational
Type
Database engine
Embedded library
Status
Active
Active
Developer
Apache Software Foundation
Apache Software Foundation
First release
2016
2023
Latest version
1.18.1 (Jan 9, 2026)
2.1.0 (Oct 12, 2026)
License
Open source (permissive)Apache License 2.0
Open source (permissive)Apache License 2.0
Free tier
✓
✓
popularity
WhichDB Score
18.6
14.2
Rank
#176
#237
GitHub stars
1.9K
3.4K
Stack Overflow questions
130
–
scale & guarantees
Comfortable data size
Large (≤ 1 PB)
Massive (Multi-PB)
Horizontal scaling
Native
Native
Multi-region
None
None
ACID
Limited
Full
Consistency
Strong
Strong
Schema
Strict
Strict
Architecture
Distributed
Distributed, Shared storage
deployment
Self-hosted
✓
✓
Vendor-managed cloud
—
—
Serverless
—
—
Embedded
—
✓
Edge
—
—
Clouds
–
–
capabilities
Transactions
—
✓
Joins
—
—
Secondary indexes
—
—
Full-text search
—
—
Vector search
—
—
Geospatial
—
—
JSON
—
—
Change data capture
—
✓
Triggers
—
—
Stored procedures
—
—
Materialized views
—
—
TTL / expiry
—
—
Pub/Sub
—
—
Columnar storage
✓
✓
In-memory
—
—
Durable persistence
✓
✓
Compression
✓
✓
Encryption at rest
✓
—
Role-based access
✓
—
Time travel
—
✓
Branching
—
✓
Online schema changes
✓
—
Streaming ingest
—
✓
Offline sync
—
—
Runs in WebAssembly
—
—
interfaces
Query languages
Impala SQL, Spark SQL, Kudu client API
SQL (via Flink, Spark, Trino, StarRocks, Doris)
Client languages
C++, Java, Python
Java, Python
Best for
Real-time analytics, OLAP, Time-series
Lakehouse, Streaming, Real-time analytics

Apache Kudu vs Apache Paimon: FAQ

What is the difference between Apache Kudu and Apache Paimon?

Apache Kudu: Columnar storage engine for fast analytics on fast-changing data, paired with Impala or Spark. Apache Paimon: Streaming lakehouse table format with LSM-based upserts and changelogs, born in Apache Flink. Apache Paimon is built for larger datasets (Multi-PB) than Apache Kudu (≤ 1 PB). Transactions: Apache Kudu offers limited ACID; Apache Paimon offers full. Only Apache Kudu has encryption at rest, role-based access, online schema changes.

When should I choose Apache Kudu over Apache Paimon?

Choose Apache Kudu for mutable fact tables in Cloudera, near-real-time analytics with Impala and time-series with updates. Choose Apache Paimon for streaming CDC from databases into the lake, real-time lakehouse with Flink and incremental ETL with changelogs.

Is Apache Kudu or Apache Paimon more popular?

Apache Kudu ranks #176 on the WhichDB Score (18.6/100) and Apache Paimon ranks #237 (14.2/100). Popularity is not fit: check the requirements that matter for your project.

Are Apache Kudu and Apache Paimon open source?

Yes. Apache Kudu is open source. License: Apache License 2.0. Yes. Apache Paimon is open source. License: Apache License 2.0.