Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External Iceberg Reads (uniform) on Delta tables (including Streaming Tables and Materialized Views via compatibility mode), configuring external engines to read Databricks tables via Unity Catalog IRC, integrating with Snowflake catalog to read Foreign Iceberg tables
Databricks provides multiple ways to work with Apache Iceberg: native managed Iceberg tables, UniForm for Delta-to-Iceberg interoperability, and the Iceberg REST Catalog (IRC) for external engine access.
write.metadata.path or write.metadata.previous-versions-max — Databricks manages metadata locations automatically; overriding causes corruptionPARTITIONED BY and CLUSTER BY produce the same Iceberg metadata for external engines — UC maintains an Iceberg partition spec with partition fields corresponding to the clustering keys, so external engines reading via IRC see a partitioned Iceberg table (not Hive-style, but proper Iceberg partition fields) and can prune on those fields; internally UC uses those fields as liquid clustering keys; the only differences between the two syntaxes are: (1) is standard Iceberg DDL (any engine can create the table), while is DBR-only DDL; (2) DV/row-tracking properties, while requires manual TBLPROPERTIES on v2PARTITIONED BYCLUSTER BYPARTITIONED BYCLUSTER BYbucket(), years(), months(), days(), hours()) with PARTITIONED BY on managed Iceberg tables — only plain column references are supported; expression transforms cause errorsCLUSTER BY on Iceberg v2 tables — set 'delta.enableDeletionVectors' = false and 'delta.enableRowTracking' = false in TBLPROPERTIES (Iceberg v3 handles this automatically; PARTITIONED BY handles this automatically on both v2 and v3)| Concept | Summary |
|---|---|
| Managed Iceberg Table | Native Iceberg table created with USING ICEBERG — full read/write in Databricks and via external Iceberg engines |
| External Iceberg Reads (Uniform) | Delta table that auto-generates Iceberg metadata — read as Iceberg externally, write as Delta internally |
| Compatibility Mode | UniForm variant for streaming tables and materialized views in SDP pipelines |
| Iceberg REST Catalog (IRC) | Unity Catalog's built-in REST endpoint implementing the Iceberg REST Catalog spec — lets external engines (Spark, PyIceberg, Snowflake) access UC-managed Iceberg data |
| Iceberg v3 | Next-gen format (Beta, DBR 17.3+) — deletion vectors, VARIANT type, row lineage |
-- No clustering
CREATE TABLE my_catalog.my_schema.events
USING ICEBERG
AS SELECT * FROM raw_events;
-- PARTITIONED BY (recommended for cross-platform): standard Iceberg syntax, works on EMR/OSS Spark/Trino/Flink
-- auto-disables DVs and row tracking — no TBLPROPERTIES needed on v2 or v3
CREATE TABLE my_catalog.my_schema.events
USING ICEBERG
PARTITIONED BY (event_date)
AS SELECT * FROM raw_events;
-- CLUSTER BY on Iceberg v2 (DBR-only syntax): must manually disable DVs and row tracking
CREATE TABLE my_catalog.my_schema.events
USING ICEBERG
TBLPROPERTIES (
'delta.enableDeletionVectors' = false,
'delta.enableRowTracking' = false
)
CLUSTER BY (event_date)
AS SELECT * FROM raw_events;
-- CLUSTER BY on Iceberg v3 (DBR-only syntax): no TBLPROPERTIES needed
CREATE TABLE my_catalog.my_schema.events
USING ICEBERG
TBLPROPERTIES ('format-version' = '3')
CLUSTER BY (event_date)
AS SELECT * FROM raw_events;
ALTER TABLE my_catalog.my_schema.customers
SET TBLPROPERTIES (
'delta.columnMapping.mode' = 'name',
'delta.enableIcebergCompatV2' = 'true',
'delta.universalFormat.enabledFormats' = 'iceberg'
);
| Table Type | Databricks Read | Databricks Write | External IRC Read | External IRC Write |
|---|---|---|---|---|
Managed Iceberg (USING ICEBERG) | Yes | Yes | Yes | Yes |
| Delta + UniForm | Yes (as Delta) | Yes (as Delta) | Yes (as Iceberg) | No |
| Delta + Compatibility Mode | Yes (as Delta) | Yes | Yes (as Iceberg) | No |
| File | Summary | Keywords |
|---|---|---|
| 1-managed-iceberg-tables.md | Creating and managing native Iceberg tables — DDL, DML, Liquid Clustering, Predictive Optimization, Iceberg v3, limitations | CREATE TABLE USING ICEBERG, CTAS, MERGE, time travel, deletion vectors, VARIANT |
| 2-uniform-and-compatibility.md | Making Delta tables readable as Iceberg — UniForm for regular tables, Compatibility Mode for streaming tables and MVs | UniForm, universalFormat, Compatibility Mode, streaming tables, materialized views, SDP |
| 3-iceberg-rest-catalog.md | Exposing Databricks tables to external engines via the IRC endpoint — auth, credential vending, IP access lists | IRC, REST Catalog, credential vending, EXTERNAL USE SCHEMA, PAT, OAuth |
| 4-snowflake-interop.md | Bidirectional Snowflake-Databricks integration — catalog integration, foreign catalogs, vended credentials | Snowflake, catalog integration, external volume, vended credentials, REFRESH_INTERVAL_SECONDS |
| 5-external-engine-interop.md | Connecting PyIceberg, OSS Spark, AWS EMR, Apache Flink, and Kafka Connect via IRC | PyIceberg, OSS Spark, EMR, Flink, Kafka Connect, pyiceberg.yaml |
| Issue | Solution |
|---|---|
| No Change Data Feed (CDF) | CDF is not supported on managed Iceberg tables. Use Delta + UniForm if you need CDF. |
| UniForm async delay | Iceberg metadata generation is asynchronous. After a write, there may be a brief delay before external engines see the latest data. Check status with DESCRIBE EXTENDED table_name. |
| Compression codec change | Managed Iceberg tables use zstd compression by default (not snappy). Older Iceberg readers that don't support zstd will fail. Verify reader compatibility or set write.parquet.compression-codec to snappy. |
| Snowflake 1000-commit limit | Snowflake's Iceberg catalog integration can only see the last 1000 Iceberg commits. High-frequency writers must compact metadata or Snowflake will lose visibility of older data. |
| Deletion vectors with UniForm | UniForm requires deletion vectors to be disabled (delta.enableDeletionVectors = false). If your table has deletion vectors enabled, disable them before enabling UniForm. |
| No shallow clone for Iceberg | SHALLOW CLONE is not supported for Iceberg tables. Use DEEP CLONE or CREATE TABLE ... AS SELECT instead. |
| Version mismatch with external engines | Ensure external engines use an Iceberg library version compatible with the format version of your tables. Iceberg v3 tables require Iceberg library 1.9.0+. |