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PyPI · #2032 most downloaded on PyPI
The Databricks adapter plugin for dbt
Last release 3 days ago
01 Oct 2026
Ships fairly regularly
a new release about every 3 weeks
Nearly every release is documented
notes for 60 of the last 60 stable releases
10 versions withdrawn
withdrawn after publishing
5 years old
186 releases · first in 2021
One column per quarter.
Nothing published for this version
Add support for Serverless jobs / refactor api usage by @benc-db in https://github.com/databricks/dbt-databricks/pull/706
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.8.7...v1.9.0b1
Add config for generating unique tmp table names for enabling parallel replace-where (thanks @huangxingyi-git!) (811)
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.8.6...v1.8.7
Fix table comment updates by @henlue in https://github.com/databricks/dbt-databricks/pull/750
parent to to and parent_columns to to_columns by @benc-db in https://github.com/databricks/dbt-databricks/pull/789Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.8.5...v1.8.6
parent to to and parent_columns to to_columns (789)Fix capitalization issue by @benc-db in https://github.com/databricks/dbt-databricks/pull/742
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.8.4...v1.8.5
Nothing published for this version
Fix dbt seed command error when seed file is partially defined in the config file by @kass-artur in https://github.com/databricks/dbt-databricks/pull/
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.8.3...v1.8.4
dbt seed command failing for a seed file when the columns for that seed file were partially defined in the properties file. (thanks @kass-artur!) (724)Fix #712: missing catalog in one metadata gathering query by @benc-db in https://github.com/databricks/dbt-databricks/pull/714
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.8.2...v1.8.3
Reverting the decision to remove 'spark.sql.sources.partitionOverwriteMode = DYNAMIC' for insert_overwrite by @benc-db in https://github.com/databrick
system catalog for metadata gathering to fix bug with renamed catalogs by @benc-db in https://github.com/databricks/dbt-databricks/pull/692Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.8.1...v1.8.2
dbt-databricks (704)Nothing published for this version
Nothing published for this version
Support Liquid Clustering for python models (663)
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.8.0...v1.8.1
Nothing published for this version
Nothing published for this version
This release carries a substantial structural change as it is the first release after moving to the new 'decoupled' dbt architecture; while today we r
This release carries a substantial structural change as it is the first release after moving to the new 'decoupled' dbt architecture; while today we retain a dependence on dbt-core so that users do not need to install/specify versions for both libraries, we have moved to depending on a shared abstraction layer between the adapter and dbt-core. As a result, we no longer need to match our feature version to that of dbt-core, and are free to adopt semantic versioning. No more releasing significant features like 'compute-per-model` as a patch version!
This release also brings improvements to the declaration and operation of Materialized Views and Streaming Tables, including the ability to schedule automatic refreshes.
A new feature introduced in this release is support for tags. To distinguish from dbt tags, which are metadata that is often used for selecting models in a dbt operation, these tags are named as databricks_tags in the model configuration.
Big thanks to dbt Labs for significant help during the development and testing of this release.
on_config_change for materialized views, expand the supported config options (536)on_config_change for streaming tables, expand the supported config options (569)In concert with dbt Labs, we've decided the next release will be 1.8.0 after all. This release pulls everything that was in the 2.0.latest branch into
In concert with dbt Labs, we've decided the next release will be 1.8.0 after all. This release pulls everything that was in the 2.0.latest branch into 1.8.latest.
Everything from the 1.7.latest branch that had not yet made it to 1.8.latest
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.8.0b2...v1.8.0rc1
This release exists for dbt Labs to test compatibility with 1.8.0 changes. Of note, with this release you will need to pip install dbt-core==1.8.0b1 a
This release exists for dbt Labs to test compatibility with 1.8.0 changes. Of note, with this release you will need to pip install dbt-core==1.8.0b1 alongside this library in order to run dbt. This is consistent with the expected behavior for 1.8.0.
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.8.0b1...v1.8.0b2
This beta brings expanded support for materialized views and streaming tables. See this discussion for full details.
This beta brings expanded support for materialized views and streaming tables. See this discussion for full details.
Backport close cursor code to 1.7 to silence cursor destructor warnings by @benc-db in https://github.com/databricks/dbt-databricks/pull/746
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.16...v1.7.17
It was brought to my attention that 1.7.15 didn't fix what it was intended to; this version should actually fix the issue where instead of getting a m
It was brought to my attention that 1.7.15 didn't fix what it was intended to; this version should actually fix the issue where instead of getting a meaningful exception message when a connection could be created, you would get the message 'conn is referenced before being initialized'.
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.15...v1.7.16
Backport fix to log events by @benc-db in https://github.com/databricks/dbt-databricks/pull/666
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.14...v1.7.15
Note: user configurable OAuth scopes currently only work for AWS, as the Databricks SDK does not let us override them for Azure. I'm working with the
Note: user configurable OAuth scopes currently only work for AWS, as the Databricks SDK does not let us override them for Azure. I'm working with the owning team to get that addressed.
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.13...v1.7.14
Up default socket timeout to 600 by @benc-db in https://github.com/databricks/dbt-databricks/pull/625
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.11...v1.7.13
Nothing published for this version
When making catalog, don't populate column comments from Databricks (too slow) by @benc-db in https://github.com/databricks/dbt-databricks/pull/618
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.10...v1.7.11
Prefer DEFAULT to NULL when inserting into, actually validate append by @benc-db in https://github.com/databricks/dbt-databricks/pull/607
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.9...v1.7.10
Fix for U2M flow on windows (sharding long passwords) (thanks @thijs-nijhuis-shell!) (597)
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.8...v1.7.9
Fixed the behavior of the incremental schema change ignore option to properly handle the scenario when columns are dropped (thanks @case-k-git!) (580)
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.4...v1.7.8
1.7.6 was accidentally pushed with beta Materialized View code, hence the skip to 1.7.7. This release rolls back databricks-sql-connector to version 2
1.7.6 was accidentally pushed with beta Materialized View code, hence the skip to 1.7.7. This release rolls back databricks-sql-connector to version 2.9.3, but otherwise includes dbt-databricks 1.7.4 fixes, as version 3.0.x has retry behavior that was insufficient for connecting to cold SQL Warehouses. I will work on addressing that separately so that we can go back to using the 3.x branch of the sql connector.
Skipped due to incorrect files in deployed package
Skipped due to incorrect files in deployed package
Update: This version has also been pulled. Unfortunately the change was not sufficient to fix the issue for affected customers.
Update: This version has also been pulled. Unfortunately the change was not sufficient to fix the issue for affected customers.
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.4...v1.7.5
UPDATE This release has been pulled due to discovered connection issues with (non-serverless) SQL Warehouses. 1.7.5 with a fix will be released shortl
UPDATE This release has been pulled due to discovered connection issues with (non-serverless) SQL Warehouses. 1.7.5 with a fix will be released shortly.
For anyone waiting for materialized views / streaming tables updates, those will be coming in 1.7.6b1 (and possibly 1.7.6b2), but we had so many fixes piling up that we wanted to get this release out faster. In particular, pandas 2.2.0 release is causing havoc, so we are pinning to < 2.2.0.
.netrc file in the user's home directory (338).netrc file in the user's home directory (555)Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.3...v1.7.4
The big change in this release is that we fixed the issue where every single dbt action initiated a new connection to Databricks. We will now reuse a
The big change in this release is that we fixed the issue where every single dbt action initiated a new connection to Databricks. We will now reuse a connection if there is a thread-local connection that matches the compute the user has selected.
This change will be most apparent if your dbt operations are very short lived, such as tests against a small table, as there is now less time spent in connection negotiation; for longer operations, the time spent in computing and transmitting the result set is more significant than the time spent on connecting.
If for some unforeseen reason this change negatively impacts performance:
a.) You can turn it off by setting the DBT_DATABRICKS_LONG_SESSIONS environment variable to false.
b.) Please file an issue so we can investigate.
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.2...v1.7.3
The big news is that the ability to choose separate compute by model is now available. Until I get updated docs out, please look here for usage notes:
The big news is that the ability to choose separate compute by model is now available. Until I get updated docs out, please look here for usage notes: https://github.com/databricks/dbt-databricks/issues/333#issuecomment-1815334768
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.2b2...v1.7.2
This is a beta release for testing the ability to specify compute on a per model basis. For full instructions on how to use this capability, for now s
This is a beta release for testing the ability to specify compute on a per model basis. For full instructions on how to use this capability, for now see https://github.com/databricks/dbt-databricks/issues/333, where I will include the provisional instructions. DO NOT RELY ON THIS CAPABILITY FOR PRODUCTION WORKLOADS YET. We are looking for users to try out this feature and report any bugs they encounter.
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.1...v1.7.2b2
Nothing published for this version
Revert to client-side filtering for large projects in an attempt improve performance of doc generation by @benc-db (thanks @mikealfare for the help) (
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.0...v1.7.1
This release is mostly about performance and compatibility with 1.7.x of dbt-core. Expect more to come in the coming weeks for expanding config, and c
This release is mostly about performance and compatibility with 1.7.x of dbt-core. Expect more to come in the coming weeks for expanding config, and config change management, for Materialized Views and Streaming Tables.
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.7.0rc1...v1.7.0
Getting compatibility with 1.7.0 RC by @benc-db in https://github.com/databricks/dbt-databricks/pull/479
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.6.6...v1.7.0rc1
Backport: Pin Databricks SDK, Pandas by @benc-db in https://github.com/databricks/dbt-databricks/pull/612
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.6.8...v1.6.9
Backport of fix for where we were invoking create schema or not exists when the schema already exists (leading to permission issue) (529)
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.6.7...v1.6.8
## Under the Hood - Updating to dbt-spark 1.6.1
Ensure optimize is run with liquid_clustered_by by @benc-db in https://github.com/databricks/dbt-databricks/pull/463
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.6.5...v1.6.6
Add option to install Python libraries from a custom index url by @benc-db (thanks to @casperdamen123) in https://github.com/databricks/dbt-databricks
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.6.4...v1.6.5
Update m2m OAuth scopes to fix bug in AWS m2m OAuth flow by @benc-db in https://github.com/databricks/dbt-databricks/pull/445
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.6.3...v1.6.4
Improve python stacktrace rendering in dbt.log by @benc-db in https://github.com/databricks/dbt-databricks/pull/434
fetchmany, resolves #408 by @NodeJSmith in https://github.com/databricks/dbt-databricks/pull/440Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.6.2...v1.6.3
fetchmany, resolves #408 (Thanks @NodeJSmith) (#409)Liquid Clustering config for table materialization
DBT_DESCRIBE_TABLE_2048_CHAR_BYPASS to true to enable this behaviour.liquid_clustered_by config to enable Liquid Clustering for Delta-based dbt models (Thanks @ammarchalifah) (#398).Revert change from #326 as it breaks DESCRIBE table in cases where the dbt API key does not have access to all tables in the schema
Added support for materialized_view and streaming_table materializations
limit command-line flagdbt debugAdded support for materialized_view and streaming_table materializations
limit command-line flagNothing published for this version
Nothing published for this version
This release is to declare that the 1.5.x branch is not compatible with databricks-sql-connector version 3.0.0
This release is to declare that the 1.5.x branch is not compatible with databricks-sql-connector version 3.0.0
Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.5.6...v1.5.7
Backport fixes from 1.6.x by @benc-db in https://github.com/databricks/dbt-databricks/pull/465
Includes the following:
fetchmany, resolves #408 (Thanks @NodeJSmith) (#409)DBT_DESCRIBE_TABLE_2048_CHAR_BYPASS to true to enable this behaviour.Full Changelog: https://github.com/databricks/dbt-databricks/compare/v1.5.5...v1.5.6
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Fixed issue where starting a terminated cluster in the python path would never return
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