Support matrix | ClickHouse Docs

This page provides comprehensive support matrices for ClickHouse’s data lake integrations. It covers the features available for each open table format, the catalogs ClickHouse can connect to, and the capabilities supported by each catalog.

Support is shown separately for ClickHouse Cloud and self-managed ClickHouse. Experimental features are disabled by default in ClickHouse Cloud and must be requested through Support. Availability is feature-dependent, so contact Support to confirm whether a specific experimental feature can be enabled. Deployment-specific configuration and limitations are described in the Notes column.

Open table format support

ClickHouse integrates with four open table formats: Apache Iceberg, Delta Lake, Apache Hudi, and Apache Paimon. Select a format below to view its support matrix.

Legend: ✅ Supported | ⚠️ Partial, experimental, or deprecated | ❌ Not supported

Feature	ClickHouse Cloud	Self-managed	Notes
Storage backends
AWS S3	✅	✅	Via `icebergS3()` or `iceberg()` alias
GCS	✅	✅	Via `icebergS3()` or `iceberg()` alias
Azure Blob Storage	✅	✅	Via `icebergAzure()`
HDFS	❌	⚠️ Deprecated	Via `icebergHDFS()`
Local filesystem	❌	✅	Via `icebergLocal()`
Access methods
Table function	✅	✅	`icebergS3()` with variants per backend
Table engine	✅	✅	`IcebergS3` with variants per backend
Cluster-distributed reads	✅	✅	`icebergS3Cluster`, `icebergAzureCluster`, `icebergHDFSCluster`
Named collections	⚠️	✅	DDL-created named collections can be enabled on select ClickHouse Cloud services. Contact Support to confirm availability.

Read features
Read support	✅	✅	Full `SELECT` support with all ClickHouse SQL functions
Partition pruning	✅	✅	See Partition pruning.
Hidden partitioning	✅	✅	Iceberg transform-based partitioning supported
Partition evolution	✅	✅	Reading tables with changing partition specs over time supported
Schema evolution	✅	✅	Column addition, removal, and reordering. See Schema evolution.
Type promotion / widening	✅	✅	`int` → `long`, `float` → `double`, `decimal(P,S)` → `decimal(P',S)` where P’ > P. See Schema evolution.
Time travel / snapshots	✅	✅	Via `iceberg_timestamp_ms` or `iceberg_snapshot_id` settings. See Time travel.
Position deletes	✅	✅	See Processing deleted rows.
Equality deletes	✅	✅	Table functions and table engines, from v25.8+. See Processing deleted rows.
Merge-on-read	⚠️	⚠️	Experimental. Supported for delete operations.
Format versions	⚠️ Partial	⚠️ Partial	v1 and v2 are supported. V3 support is partial; deletion vectors and manifest compaction aren’t supported.
Column statistics	✅	✅
Bloom filters / puffin files	❌	❌	Bloom filter indexes in Puffin files aren’t supported
Virtual columns	✅	✅	`_path`, `_file`, `_size`, `_time`, `_etag`. See Virtual columns.

Write features
Table creation	⚠️	⚠️	Experimental. Requires `allow_insert_into_iceberg = 1`. From v25.7+. See Creating a table.
`INSERT`	✅ Beta	✅ Beta	Beta from 26.2. Requires `allow_insert_into_iceberg = 1`. See Inserting data.
`DELETE`	⚠️	⚠️	Experimental. Requires `allow_insert_into_iceberg = 1`. Via `ALTER TABLE ... DELETE WHERE`. See Deleting data.
`ALTER TABLE` (schema changes)	⚠️	⚠️	Experimental. Requires `allow_insert_into_iceberg = 1`. Add, drop, modify, rename columns. See Schema evolution.
Compaction	⚠️	⚠️	Experimental. Requires `allow_experimental_iceberg_compaction = 1`. See Compaction.
`UPDATE` / `MERGE`	❌	❌	Not supported. See Compaction.
Copy-on-write	❌	❌	Not supported
Expire snapshots	⚠️	⚠️	Experimental. Requires Iceberg v2, `allow_insert_into_iceberg = 1`, and `allow_experimental_expire_snapshots = 1`. See Expire snapshots.
Remove orphan files	⚠️	⚠️	Experimental. Requires Iceberg v2 or higher, `allow_insert_into_iceberg = 1`, and `allow_iceberg_remove_orphan_files = 1`. See Remove orphan files.
Writing partitions	✅ Beta	✅ Beta	Supported for Iceberg writes.
Altering partitions	❌	❌	Changing the partitioning scheme from ClickHouse isn’t supported. ClickHouse can write to Iceberg tables with an evolved partitioning scheme.

Metadata
Branching and tagging	❌	❌	Iceberg branch/tag references aren’t supported
Metadata file resolution	✅	✅	Supports resolution through catalogs, directory listing, `version-hint`, and a specific path. See Metadata file resolution.
Data caching	✅	✅	Same mechanism as S3/Azure/HDFS storage engines. See Data cache.
Metadata caching	✅	✅	Enabled by default via `use_iceberg_metadata_files_cache`. See Metadata cache.

From version 25.6, ClickHouse reads Delta Lake tables using the Delta Lake Rust kernel, providing broader feature support; however, known issues occur when accessing data in Azure Blob Storage. For this reason the Kernel is disabled when reading data on Azure Blob Storage. We indicate below which features require this kernel.

Feature	ClickHouse Cloud	Self-managed	Notes
Storage backends
AWS S3	✅	✅	Via `deltaLake()` or `deltaLakeS3()`
GCS	✅	✅	Via `deltaLake()` or `deltaLakeS3()`
Azure Blob Storage	✅	✅	Via `deltaLakeAzure()`
HDFS	❌	❌	Not supported
Local filesystem	❌	✅	Via `deltaLakeLocal()`
Access methods
Table function	✅	✅	`deltaLake()` with variants per backend
Table engine	✅	✅	`DeltaLake`
Cluster-distributed reads	✅	✅	`deltaLakeCluster`, `deltaLakeAzureCluster`
Named collections	⚠️	✅	DDL-created named collections can be enabled on select ClickHouse Cloud services. Contact Support to confirm availability.
Read features
Read support	✅	✅	Full `SELECT` support with all ClickHouse SQL functions
Partition pruning	✅ Beta	✅ Beta	Requires the Delta Kernel.
Schema evolution	✅ Beta	✅ Beta	Requires the Delta Kernel.
Time travel	✅ Beta	✅ Beta	Requires the Delta Kernel.
Deletion vectors	✅	✅
Column mapping	✅	✅
Change data feed	✅ Beta	✅ Beta	Requires the Delta Kernel.
Virtual columns	✅	✅	`_path`, `_file`, `_size`, `_time`, `_etag`. See Virtual columns.
Write features
`INSERT`	✅ Beta	✅ Beta	Requires the Delta Kernel. Writes are supported for S3 and GCS; Azure writes aren’t supported. Requires `allow_delta_lake_writes = 1` from v26.7; on earlier versions, use `allow_experimental_delta_lake_writes = 1`. See Delta Lake writes.
`DELETE` / `UPDATE` / `MERGE`	❌	❌	Not supported
Create empty table	❌	❌	The `CREATE TABLE` operation assumes that the Delta Lake table already exists on object storage.
Caching
Data caching	✅	✅	Same mechanism as S3/Azure/HDFS storage engines. See Data cache.

Feature	ClickHouse Cloud	Self-managed	Notes
Storage backends
AWS S3	✅	✅	Via `hudi()`
GCS	✅	✅	Via `hudi()`
Azure Blob Storage	❌	❌	Not supported
HDFS	❌	❌	Not supported
Local filesystem	❌	❌	Not supported
Access methods
Table function	✅	✅	`hudi()`
Table engine	✅	✅	`Hudi`
Cluster-distributed reads	✅	✅	`hudiCluster` (S3 only)
Named collections	⚠️	✅	DDL-created named collections can be enabled on select ClickHouse Cloud services. Contact Support to confirm availability.
Read features
Read support	✅	✅	Full `SELECT` support with all ClickHouse SQL functions
Schema evolution	❌	❌	Not supported
Time travel	❌	❌	Not supported
Virtual columns	✅	✅	`_path`, `_file`, `_size`, `_time`, `_etag`. See Virtual columns.
Write features
`INSERT` / `DELETE` / `UPDATE`	❌	❌	Read-only integration
Caching
Data caching	❌	❌	Not supported

Feature	ClickHouse Cloud	Self-managed	Notes
Storage backends
S3	⚠️	⚠️	Experimental. Via `paimon()` or `paimonS3()`
GCS	⚠️	⚠️	Experimental. Via `paimon()` or `paimonS3()`
Azure Blob Storage	⚠️	⚠️	Experimental. Via `paimonAzure()`
HDFS	❌	⚠️	Experimental and deprecated. Via `paimonHDFS()`
Local filesystem	❌	⚠️	Experimental. Via `paimonLocal()`
Access methods
Table function	⚠️	⚠️	Experimental. `paimon()` with variants per backend
Table engine	⚠️	⚠️	Experimental. `Paimon` with variants per backend. Requires `allow_experimental_paimon_storage_engine = 1`.
Cluster-distributed reads	⚠️	⚠️	Experimental. `paimonS3Cluster`, `paimonAzureCluster`, `paimonHDFSCluster`
Named collections	⚠️	⚠️	Experimental. DDL-created named collections can be enabled on select ClickHouse Cloud services. Contact Support to confirm availability.
Read features
Read support	⚠️	⚠️	Experimental. Full `SELECT` support with all ClickHouse SQL functions
Schema evolution	❌	❌	Not supported
Time travel	❌	❌	Not supported
Virtual columns	⚠️	⚠️	Experimental. `_path`, `_file`, `_size`, `_time`, `_etag`. See Virtual columns.
Write features
`INSERT` / `DELETE` / `UPDATE`	❌	❌	Read-only integration
Caching
Data caching	❌	❌	Not supported

Catalog support

ClickHouse can connect to external data catalogs using the DataLakeCatalog database engine, which exposes the catalog as a ClickHouse database. Tables registered in the catalog appear automatically and can be queried with standard SQL.

The following catalogs are currently supported. Refer to each catalog’s reference guide for full setup instructions.

Catalog	Formats	Read	Create table	INSERT	Reference guide
AWS Glue Catalog	Iceberg	✅ Beta	❌	❌	Glue catalog guide
BigLake Metastore	Iceberg	✅ Beta	❌	❌	BigLake Metastore guide
Databricks Unity Catalog	Delta, Iceberg	✅ Beta	✅ Beta	✅ Beta	Unity Catalog guide
Iceberg REST	Iceberg	✅ Beta	❌	❌	REST catalog guide
Lakekeeper	Iceberg	✅ Beta	❌	❌	Lakekeeper catalog guide
Project Nessie	Iceberg	✅ Experimental	❌	❌	Nessie catalog guide
Microsoft OneLake	Iceberg	✅ Beta	✅ Beta	✅ Beta	OneLake catalog guide
SeaweedFS	Iceberg	✅ Beta	✅ Beta	✅ Beta	SeaweedFS catalog guide

All catalog integrations currently require an experimental or beta setting to be enabled. With the exception of Microsoft OneLake, Databricks Unity Catalog, and SeaweedFS, all catalogs expose read-only access — tables can be queried but not created or written to through the catalog connection. To load data from a catalog into ClickHouse for faster analytics, use INSERT INTO SELECT as described in the accelerating analytics guide. To write data back to open table formats, create standalone Iceberg tables as described in the writing data guide.