Apache Hudi vs Delta Lake: Lakehouse Table Format Comparison

Overview

Apache Hudi (Hadoop Upserts Deletes and Incrementals) was created at Uber and donated to Apache. It was designed specifically for use cases requiring frequent upserts and deletes on large datasets — streaming ingestion from Kafka, CDC pipelines from databases. Its MergeOnRead (MOR) table type enables low-latency writes by appending delta logs and merging lazily during reads, making it particularly efficient for high-frequency update workloads.

Delta Lake, created by Databricks, focuses on the broader lakehouse use case — bringing database-grade reliability to data lakes with ACID transactions, schema enforcement, and time travel. Its deeper Databricks integration means it benefits from ongoing Databricks optimizations and ecosystem tools. Delta Lake has broader adoption but Hudi has a technical edge for specific streaming and upsert-heavy workloads.

Key Technical Differences

Hudi's dual table type architecture is its most distinctive feature. Copy-on-Write (COW) tables rewrite files on every update — suitable for batch workloads. MergeOnRead (MOR) tables append delta logs for writes and merge during reads, providing near-real-time availability of ingested data (minutes rather than hours) at the cost of slightly higher read overhead. This flexibility makes Hudi the better choice for CDC and streaming ingestion use cases.

Delta Lake's write path is simpler — all writes go through a single model (copy-on-write by default, optimized merge available). On Databricks, Auto Optimize and Auto Compaction manage file sizes automatically. Off Databricks, manual OPTIMIZE and VACUUM commands are needed. Hudi's compaction scheduling for MOR tables requires more configuration but provides better control over the read/write performance tradeoff.

CDC pipeline optimization is Hudi's strongest use case. When ingesting Change Data Capture streams from databases (via Debezium + Kafka), Hudi's upsert path handles insert/update/delete records efficiently, respecting primary keys and applying changes at record level. Delta Lake supports this via MERGE operations but with higher write amplification.

Performance & Scale

For read-heavy analytics, Delta Lake's consistent file layout and Databricks optimizations typically provide better query performance. For write-heavy workloads with frequent upserts, Hudi's MOR tables provide better write latency. Both scale to petabyte-size tables across distributed storage. AWS EMR provides first-class support for Hudi with AWS-optimized configurations.

When to Choose Each

Choose Hudi when your primary use case is CDC ingestion from databases, near-real-time streaming to a data lake, or when your platform is AWS EMR. Its upsert optimization and MOR table type provide genuine technical advantages for these scenarios that Delta Lake and Iceberg do not fully match.

Choose Delta Lake when your platform is Databricks, when your workload is predominantly batch analytics with occasional updates, or when you want the broadest ecosystem tooling and community resources. Delta Lake's Databricks integration is difficult to replicate with other tools.

Bottom Line

Hudi wins for high-frequency upsert and CDC streaming scenarios, particularly on AWS. Delta Lake wins for Databricks-based platforms and batch analytics use cases. Iceberg wins for engine-agnostic multi-compute architectures. All three formats are converging in capabilities — the platform and primary workload type should drive the choice.