--- title: Databricks - Delta Deployment with Liquibase description: Deploy the changes of a Databricks DEV catalog to a PROD catalog that holds data, with Liquibase Community in Docker. --- [Skip to content](https://knowledge.bigenius-x.com/databricks-delta-deployment-with-liquibase#main-content) [Contact us](https://knowledge.bigenius-x.com/kb-tickets/new?hsLang=en) [Go to Customer Portal](https://knowledge.bigenius-x.com/customer-portal?hsLang=en) [![TRI\_Logo\_biGENIUS\_2020\_RGB\_s](https://knowledge.bigenius-x.com/hs-fs/hubfs/Marketing/TRI_Logo_biGENIUS_2020_RGB_s.png?width=150&height=42&name=TRI_Logo_biGENIUS_2020_RGB_s.png)](https://www.bigenius-x.com/) Open main navigation Close main navigation - [Contact us](https://knowledge.bigenius-x.com/kb-tickets/new) - [Go to Customer Portal](https://knowledge.bigenius-x.com/customer-portal) How can we help you? - There are no suggestions because the search field is empty. 1. [biGENIUS-X Knowledge Base](https://knowledge.bigenius-x.com/?hsLang=en) 2. [Generators](https://knowledge.bigenius-x.com/generators?hsLang=en) 3. [Delta Deployment](https://knowledge.bigenius-x.com/generators?hsLang=en#delta-deployment) # Databricks - Delta Deployment with Liquibase **Applies to** Platform: Databricks Deploy the changes of a Databricks DEV catalog to a PROD catalog that holds data, with Liquibase Community in Docker. Liquibase writes the differences of the tables as SQL files that you review and run in PROD, and a Python script exports the views of DEV into a notebook. Afterwards PROD has the structure of DEV. biGENIUS-X itself always deploys the complete solution, see [Do a delta deployment](https://knowledge.bigenius-x.com/do-a-delta-deployment?hsLang=en). ### Example environment The article uses two catalogs of one Databricks workspace with Unity Catalog, deployed from the same Data Vault project: - DEV, catalog `datalakehouse_liquibase_dev`: holds the new version, deployed and loaded. Compared with PROD, it adds the Hub `CURRENCY`, renames the term `LINETOTAL` of the Satellite `ORDERDETAIL` to `TOTAL`, and no longer has the Reference Table `CURRENCY`. - PROD, catalog `datalakehouse_liquibase_prod`: holds the previous version with its loaded data. The Liquibase Databricks library has two limitations: catalog names must not contain hyphens (`-`), and the schema names of DEV and PROD must differ, for example `c3dbdv_rawvault_dev` and `c3dbdv_rawvault_prod`. Each catalog is reached with its own [personal access token](https://docs.databricks.com/en/dev-tools/auth/pat.html). The [Databricks - Target environment with Unity Catalog](https://knowledge.bigenius-x.com/databricks-unity-target-environment?hsLang=en) describes how to prepare the catalogs. ### Prerequisites The machine that runs Liquibase needs Docker, for example Docker Desktop. #### Liquibase folder structure Create a folder, here `C:\PROJECTS\liquibase_demo`, with the subfolder `liquibase` and its subfolders `changelog`, `conf` and `lib`: ![Liquibase folder with the changelog, conf and lib subfolders](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Feb-03-2026-09-32-54-2464-AM.png?width=640&height=190&name=image-png-Feb-03-2026-09-32-54-2464-AM.png) #### Drivers Download into `lib`: - The latest `liquibase-databricks-.jar` from the [liquibase-databricks releases](https://github.com/liquibase/liquibase-databricks/releases/). - `DatabricksJDBC42.jar` from [DatabricksJDBC42-2.7.3.1010.zip](https://databricks-bi-artifacts.s3.us-east-2.amazonaws.com/simbaspark-drivers/jdbc/2.7.3/DatabricksJDBC42-2.7.3.1010.zip). The current [Databricks JDBC driver](https://www.databricks.com/spark/jdbc-drivers-download) does not work here. Liquibase Databricks 1.4.2 does not contain the fix of [issue 340](https://github.com/liquibase/liquibase-databricks/issues/340), so `EnableArrow=0` is set twice and the connection fails. #### Properties Create one property file per schema in `conf`. The HTTP path is shown in the cluster settings under Advanced, JDBC/ODBC. For example, `liquibase-rawvault.properties` for the Raw Vault schemas: ``` # Target DB = PROD (where changes are applied) url=jdbc:databricks://:443/default;transportMode=http;ssl=1;httpPath=;AuthMech=3;ConnCatalog=datalakehouse_liquibase_prod;ConnSchema=c3dbdv_rawvault_prod username=token password= defaultCatalogName=datalakehouse_liquibase_prod defaultSchemaName=c3dbdv_rawvault_prod # Reference DB = DEV (source of truth) referenceUrl=jdbc:databricks://:443/default;transportMode=http;ssl=1;httpPath=;AuthMech=3;ConnCatalog=datalakehouse_liquibase_dev;ConnSchema=c3dbdv_rawvault_dev referenceUsername=token referencePassword= referenceDefaultCatalogName=datalakehouse_liquibase_dev referenceDefaultSchemaName=c3dbdv_rawvault_dev # Databricks driver driver=com.databricks.client.jdbc.Driver liquibase.includedSnapshotTypes=TABLE,COLUMN,PRIMARYKEY,INDEX,SCHEMA ``` Here `` is the host name of the workspace URL, `` the HTTP path of the cluster, and the tokens are the personal access tokens of PROD and DEV. #### Python Install [Python](https://www.python.org/downloads/) and add it to `PATH` during the installation. ### Generate the delta deployment scripts Generate the scripts in two parts with the [official Liquibase Docker image](https://hub.docker.com/r/liquibase/liquibase): all database objects except the views, then the views. The Liquibase Databricks library does not handle view definitions well, so a Python script exports the views. #### All database objects except the views 1. Open PowerShell 7 in `C:\PROJECTS\liquibase_demo`. 2. Generate the changelog XML file for one schema. Run the command once per schema, with its property file and XML file; Liquibase cannot compare all schemas in one run. ``` docker run --rm ` -v "$PWD\liquibase\conf:/liquibase/conf" ` -v "$PWD\liquibase\changelog:/liquibase/changelog" ` -v "$PWD\liquibase\lib:/liquibase/lib" ` liquibase/liquibase ` --defaultsFile=/liquibase/conf/liquibase-rawvault.properties ` --changeLogFile=/liquibase/changelog/dev_to_prod_all_except_views_rawvault.xml ` --diffTypes=tables,columns,indexes,foreignkeys,primarykeys,uniqueconstraints,sequences ` --log-level=warning diffChangeLog ``` The run ends with `Liquibase command 'diffChangelog' was executed successfully.` ![Liquibase console output ending with diffChangelog executed successfully](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Feb-03-2026-10-16-58-2076-AM.png?width=433&height=290&name=image-png-Feb-03-2026-10-16-58-2076-AM.png) 3. Check that `changelog\dev_to_prod_all_except_views_rawvault.xml` lists the changes, for example a `createTable` for the new Hub: ![Changelog folder with the file dev\_to\_prod\_all\_except\_views\_rawvault.xml](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Feb-23-2026-10-05-42-0110-AM.png?width=501&height=116&name=image-png-Feb-23-2026-10-05-42-0110-AM.png) ![Changelog XML with a createTable change for rdv\_hub\_currency](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Feb-23-2026-09-57-01-6331-AM.png?width=516&height=193&name=image-png-Feb-23-2026-09-57-01-6331-AM.png) 4. Generate the SQL file for each schema. Use the same `docker run` options up to the image name, followed by: ``` --defaultsFile=/liquibase/conf/liquibase-rawvault.properties ` --changeLogFile=dev_to_prod_all_except_views_rawvault.xml ` updateSQL > liquibase\changelog\dev_to_prod_all_except_views_rawvault.sql ``` The run ends with `Liquibase command 'updateSql' was executed successfully.` ![Liquibase console output ending with updateSql executed successfully](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Feb-03-2026-10-17-42-8978-AM.png?width=442&height=264&name=image-png-Feb-03-2026-10-17-42-8978-AM.png) 5. Open the SQL file. It contains the statements of the changes, for example `CREATE TABLE` for the new Hub: ![Changelog folder with the XML and the SQL file of the rawvault schema](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Feb-23-2026-10-05-58-0827-AM.png?width=481&height=139&name=image-png-Feb-23-2026-10-05-58-0827-AM.png) ![SQL file with CREATE TABLE datalakehouse\_liquibase\_prod.c3dbdv\_rawvault\_prod.rdv\_hub\_currency](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Feb-23-2026-10-06-30-2949-AM.png?width=640&height=56&name=image-png-Feb-23-2026-10-06-30-2949-AM.png) 6. Review each statement. Liquibase compares structures only. It writes a renamed column as a dropped and an added column, and it drops tables and columns that exist only in PROD; their data is lost. Review the generated SQL files and back up the PROD database before you run them. #### All views 1. Download [export\_databricks\_views\_v2.py](https://f926da42ec3845beb4acf72f.blob.core.windows.net/hubspotknowledgebase/export_databricks_views_v2.py) into `C:\PROJECTS\liquibase_demo`. 2. Set the values in `CONFIG` for your workspace: the Databricks CLI profile, the DEV and PROD catalogs, the DEV and PROD schema lists (by default `stage`, `rawvault`, `businessvault` and `mart`), the workspace host, the HTTP path of a SQL warehouse and `output_dir`. 3. Set the DEV personal access token in the environment variable `DATABRICKS_TOKEN`, for example `$env:DATABRICKS_TOKEN = ''`. 4. Install the libraries: `pip install databricks-sdk databricks-sql-connector`. 5. Run `python .\export_databricks_views_v2.py`. The script writes the notebook `views_to_deploy_prod_notebook.py` with one cell per view of the DEV schemas, renamed to the PROD catalog and schemas: ![Notebook cell with CREATE OR REPLACE VIEW for datalakehouse\_liquibase\_prod.c3dbdv\_rawvault\_prod.rdv\_current\_creditcard](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Feb-23-2026-01-36-31-0905-PM.png?width=640&height=191&name=image-png-Feb-23-2026-01-36-31-0905-PM.png) ### Run the delta deployment Run the files in PROD in this order: 1. In each SQL file, find every `DROP COLUMN` statement. Before it, add the column mapping for its table, with your catalog, schema and table: ``` ALTER TABLE datalakehouse_liquibase_prod.c3dbdv_rawvault_prod .rdv_sat_orderdetail SET TBLPROPERTIES ('delta.columnMapping.mode' = 'name'); ``` Delta tables need column mapping to drop a column. Liquibase writes a generic `ALTER TABLE ... DROP COLUMN` without it. 2. For each schema, copy the content of its SQL file into a new notebook in Databricks and run it. 3. Import `views_to_deploy_prod_notebook.py` into the workspace and run it. Each cell catches its error, and the last cell lists the views that failed, for example because they use a view created later. 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