Delta Lake vs Iceberg vs Hudi: Picking a Format You Can Live With

Key Takeaways

• The three formats have converged on core features, so write performance, catalog reach, and migration cost now drive the choice.
• Apache Hudi leads on write-heavy and CDC-style workloads, where its record-level index keeps write cost tied to the size of the change.
• Apache Iceberg leads on multi-engine and multi-catalog breadth, which is what keeps your compute options open later.
• Delta Lake leads for teams already committed to Databricks as their primary compute layer.
• Iceberg v3 narrows several of these gaps while leaving the underlying trade-offs intact.

The Delta Lake vs Iceberg choice usually happens early in a lakehouse build, before the workload has settled, and it quietly sets your options for years. The bill arrives later, when a second query engine or a new CDC source exposes what the format cannot do.

Switching then means rewriting downstream jobs, revalidating every query against every engine, and retraining a team on a different commit model. Write performance, catalog reach, and migration cost separate Iceberg, Delta Lake, and Apache Hudi in 2026, and each deserves more weight than a feature checklist.

What is Apache Iceberg, and What Problem Does It Solve for Data Lakes?

Apache Iceberg is an open table format that adds database-style reliability to files sitting in cheap object storage: snapshot isolation, schema evolution, and time travel. It solves the problem that a raw data lake offers no transactional guarantees and no consistent metadata across engines.

Iceberg delivers that through five metadata layers, each doing one job:

• Data files: The Parquet or ORC files that hold the rows.
• Manifest files: Track which data files belong to the table, with column-level statistics that let engines prune files before reading them.
• Manifest lists: Group manifests into one snapshot, which is what makes time travel and rollback cheap.
• Metadata file: Describes the current table state, including schema, partition spec, and snapshot history.
• Catalog: Points to the current metadata file, coordinates commits, and keeps concurrent writers consistent.
  

That last layer becomes the deciding factor later in this comparison. Iceberg tables: features, benefits, and best practices go deeper into the architecture than this comparison needs.

A table format is also what turns a data lake into a lakehouse. Without one, you have files. With one, you have tables that several engines can read and write safely. If that distinction matters on your team, data lakes vs. lakehouses covers the foundation.

What is Delta Lake, and What Problem Does it Solve for Data Lakes?

Delta Lake is an open table format that brings ACID transactions to Spark through a transaction log, the Delta log, which records every change as an ordered series of commits. It solves the same reliability problem Iceberg does, with roots in the Spark and Databricks ecosystem. Databricks built it, and it now sits under the Linux Foundation.

Its strengths cluster around the mutation work Spark teams do daily:

• Mature MERGE support: Row-level updates, deletes, and upserts have been production-grade for years, which matters when pipelines correct history instead of only appending.
  
• Streaming upserts: One Delta table serves as both a batch table and a streaming source with exactly-once semantics, so a second copy is unnecessary.
  
• Delta UniForm: External engines read Delta tables as Iceberg without duplicating the underlying files, which softens the portability question for read-only consumers.

Portability outside Databricks still depends on which Delta features and client versions are involved, so check the feature matrix against your external readers before committing.

Delta 4.x has also moved toward catalog-managed tables, where the catalog coordinates commits and holds the source of truth for table state, bringing Delta's commit model noticeably closer to Iceberg's.

What is Apache Hudi, and What Problem Does it Solve for Data Lakes?

Apache Hudi is an open table format built around a commit timeline of instant actions, designed from the start for high-frequency upserts and change data capture. It solves the problem of keeping a data lake current when records update constantly instead of only arriving.

Three design choices follow from that starting point:

• Incremental processing: Downstream pipelines request exactly the records that changed between two points on the timeline, with before and after images in CDC mode. Consumers chain off the table instead of re-reading snapshots and diffing them.
  
• Copy-on-write tables: Files are rewritten at write time, keeping reads fast and suiting tables that update less often.
• Merge-on-read tables: Deltas are appended, and the merge is deferred to query time or compaction, which suits high-frequency writes.

Record-level indexing is what makes those upserts cheap, resolving which file groups a batch touches so per-commit work scales with the size of the change instead of the size of the table.

How Do Apache Iceberg, Delta Lake, and Apache Hudi Compare on Architecture and ACID Guarantees?

All three formats now guarantee ACID transactions and schema evolution, but they reach that guarantee through different commit models. The table below shows where those models differ:

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On paper, parity is closer than most 2024-era comparisons suggest. That is exactly why write performance and catalog reach should drive the decision.

How Do Apache Iceberg, Delta Lake, and Apache Hudi Compare on Write and Upsert Performance?

Apache Hudi measures ahead on write-heavy and upsert-heavy workloads, according to Apache Hudi's 2026 performance benchmark post. Its August 2026 LakeLoader run tested Hudi 1.1.1 against Iceberg 1.10.0 and Delta Lake 3.3.2 on Spark 3.5.

On a 10 TB skewed fact table with 90% updates, Hudi averaged roughly 4 times lower incremental write latency than Iceberg and 6 times lower than Delta Lake. On a merge-on-read table taking sparse column-level updates, the gap was roughly 8 times against Iceberg and 5 times against Delta Lake. With ingestion running alongside compaction, Hudi sustained continuous writes while the other two hit snapshot-conflict retries.

Those numbers need the following caveats:

• The source has a stake in the outcome: Hudi maintainers ran and published it, though they say so openly, and the open-source harness lets you rerun it.
• The versions are uneven: Iceberg 1.11.0 shipped three months before publication, and the tested Delta Lake line sits a major version behind 4.x, which Spark 3.5 cannot run.
• Read-heavy workloads narrow the gap: The same post expects the formats to perform similarly on scan-heavy queries and append-only writes.

If your tables mutate constantly, the write path is where these formats diverge most. If your workload is append-mostly analytics, format choice is unlikely to be your bottleneck.

What's New in Apache Iceberg v3, and How Does It Change the Delta Lake and Hudi Comparison?

Apache Iceberg v3 adds deletion vectors for faster row-level deletes, row lineage for tracking changes across commits, a VARIANT type for semi-structured data, native geospatial types, default column values, and nanosecond timestamps.

Several of these have compatible counterparts in Delta Lake, letting teams interoperate between Iceberg and Delta on one copy of the data without a rewrite.

Adopting v3 has a cost: v3 readers can read v2 tables while v2 readers cannot read v3 tables. Engine support is also uneven, since some engines can read v3 tables before they can write to them, and others are still catching up.

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Which Table Format Has the Broadest Multi-Engine and Catalog Support in 2026?

Apache Iceberg has the broadest multi-engine and catalog support in 2026, with native or near-native support across Spark, Trino, Flink, and most cloud warehouses, backed by the common REST catalog standard. Delta Lake's reach outside Spark depends on UniForm and client versions, while Hudi runs first-class on Spark and Flink, with Presto and Trino support behind those.

Catalog reach is where a format decision becomes a platform decision. If your catalog speaks only to one vendor's compute, an open format underneath will not save you when the next engine arrives.

Acceldata designed xLake OS Foundry around that problem. It runs Iceberg, Delta, and Hudi tables on one open stack and works with Gravitino, Polaris, and Unity Catalog, so the format you choose for one workload does not decide the compute engine for every workload.

Acceldata hardens and supports the whole stack under a single contract, and it is built on open source, with no proprietary formats. Explore the Foundry to learn how it keeps your format choice intact through a change of engine.

How Should Enterprises Choose Between Delta Lake, Iceberg, and Hudi for a New Lakehouse?

Enterprises should choose between Delta Lake, Iceberg, and Hudi for a new lakehouse based on the dominant workload: Hudi for high-frequency CDC and upsert-heavy pipelines, Iceberg for multi-engine and multi-cloud flexibility, and Delta Lake when Databricks is the primary compute layer and likely to stay that way.

Momentum favors Iceberg, with one qualification. Ryft's 2026 State of Apache Iceberg in the Enterprise report surveyed 252 senior data leaders in January 2026, and most said they plan to migrate additional datasets to Iceberg in the next 12 months.

Every respondent already runs Iceberg in production, so the finding measures depth of commitment among existing users rather than share across formats.

The checklist below maps workload patterns to a starting format:

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What Does Migrating Between Delta Lake, Iceberg, and Hudi Cost in Practice?

Migrating between Delta Lake, Iceberg, and Hudi costs mostly engineering time and risk, because the work sits outside the data files: rewriting downstream jobs, retraining engineers on a new commit model, and validating that every query engine reads the new format correctly.

Budget for four things: an inventory of jobs and rules, a dependency map of downstream consumers, a validation pass across every engine, and a period running both formats in parallel. Validation is the line item teams underestimate, since matching row counts and business metrics across billions of rows is a project in itself.

The platform layer underneath the format matters as much as the format itself. A platform built around one proprietary catalog can make even an open format hard to leave. Parquet, Iceberg, Delta, and ORC compared walks through where that dependency forms at the storage and table layers, and the open data lakehouse and vendor lock-in covers what openness requires across catalog and compute.

Choosing an Open Table Format Strategy With Acceldata

In 2026, Iceberg, Delta Lake, and Hudi have converged closely enough that the larger risk sits one layer down. A platform that runs only one of them well forces a second migration the moment a workload changes shape, and that migration costs more than the original format decision ever did.

Three practices keep the decision reversible:

• Measure your own upsert and scan mix before weighing brand familiarity, because your write pattern is what separates these formats in practice.
• Treat catalog interoperability as a first-class requirement, since a federated, format-agnostic catalog is what keeps your engines swappable once the format is set.
• Keep the platform layer format-agnostic even after committing to one format for a given workload, so the substrate can still run all three.

xLake OS Foundry runs Iceberg, Delta Lake, and Hudi tables on the same Kubernetes-based platform, hardened and supported as one stack instead of several separately managed open-source projects.

Book a demo to see all three running side by side, without locking your team into one format or one compute engine.

FAQs

Can Iceberg, Delta Lake, and Hudi tables run inside the same data platform at the same time?

Yes, Iceberg, Delta Lake, and Hudi tables can run in the same data platform at once if its catalog is federated, not tied to one format. Teams can then keep Hudi for CDC and Iceberg for BI.

Does choosing Apache Iceberg mean giving up Delta Lake-specific features like Unity Catalog?

No, choosing Apache Iceberg does not mean giving up Unity Catalog, since UniForm and Iceberg REST support let some Unity Catalog tables be read as Iceberg. Support varies by feature and client version, so verify the features you rely on.

Which table format handles small-file compaction most efficiently by default?

Apache Hudi handles small-file compaction most efficiently by default, since it compacts alongside ingestion on its own. Iceberg and Delta Lake usually need an engine setting, scheduler, or manual trigger.

Do managed platforms like Snowflake and BigQuery support all three table formats equally well?

No, managed platforms like Snowflake and BigQuery do not support all three formats equally well. Iceberg support is broad and often native, Delta Lake runs mostly through connectors, and Hudi is least consistent outside Spark.

Is Apache Hudi still worth adopting now that Iceberg v3 has closed some of the CDC and streaming gap?

Yes, Apache Hudi is still worth adopting when continuous upserts dominate, because its incremental processing still leads on frequent writes after v3. Teams on Hudi can also use Apache XTable to expose tables to Iceberg readers.