Databricks (Edge)

Last updated on Sep 24, 2026

This Destination is currently available for Early Access. Please contact your Hevo account executive or the Support team to enable it for your team. Alternatively, request for early access to try out one or more such features.

Databricks is a cloud-based data and AI platform that provides lakehouse capabilities for storing, processing, and analyzing data. Databricks supports Delta Lake and provides SQL warehouses and other compute resources for querying and processing data.

Hevo connects to a Databricks SQL warehouse or all-purpose cluster and loads your data into tables in the catalog and schema configured for the Destination. Your workspace can be hosted on Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). The connection settings are the same for all three, and only the workspace URL differs.

Databricks supports the following data governance models:

  • Unity Catalog: The recommended data governance solution, and the default for new workspaces. Unity Catalog provides centralized governance for data across workspaces in an account. Tables use a three-level naming format, catalog.schema.table. When configuring the Destination, you must specify the catalog where Hevo will create the tables.

  • Hive Metastore: The legacy data governance solution. The metastore is associated with a workspace, and tables use a two-level naming format, schema.table. You do not specify a catalog when configuring the Destination. Hevo uses the built-in hive_metastore catalog. Databricks recommends Unity Catalog instead, and Hive Metastore is not available if legacy access is turned off for your account or workspace.

    Note: Databricks accounts created after December 18, 2025 do not have access to certain legacy features, including the Hive Metastore. These accounts use Unity Catalog instead.


Modifying Databricks Destination Configuration

You can modify some settings of your Databricks Destination after its creation. However, any configuration changes will affect all the Pipelines using that Destination.

To modify the configuration of your Databricks Destination:

  1. In the detailed view of your Destination, do one of the following:

    • Click the Destination Actions icon, and then click Edit Destination.

      Destination Actions menu

    • In the Destination Configuration section, click Edit.

      Edit the Destination Configuration

  2. On the Edit Destination page:

    Edit Destination page

    • You can specify a new name for your Destination, not exceeding 255 characters.

    • In the Connect to your Databricks section, you can update the Workspace URL, the HTTP Path, and the Catalog Name.

      The Catalog Type cannot be changed, as Hevo identifies your existing Destination tables using the naming format of the catalog type that you selected.

    • In the Authentication section, click Change next to the Personal Access Token field to clear it, and then enter your new token. Do this whenever you replace an expired token in Databricks.

  3. Click Test & Save to check the connection to your Databricks Destination and then save the modified configuration.


Data Type Evolution in Databricks Destinations

Hevo has a standardized data system that defines unified internal data types, referred to as Hevo data types. During the data ingestion phase, the Source data types are mapped to the Hevo data types, which are then transformed into the Destination-specific data types during the data loading phase. A mapping is then generated to evolve the schema of the Destination tables.

The following image illustrates the data type hierarchy applied to Databricks Destination tables:

Databricks Data Type Hierarchy

Data Type Mapping

The following table shows the mapping between Hevo data types and Databricks data types:

Hevo Data Type Databricks Data Type
BOOLEAN BOOLEAN
BYTEARRAY BINARY
-  BYTE
-  SHORT
-  INTEGER
INT
LONG BIGINT
DATE DATE
-  DATETIME
-  DATETIME_TZ
TIMESTAMP
DECIMAL -  DECIMAL
-  STRING
FLOAT FLOAT
DOUBLE DOUBLE
-  ARRAY
-  GEOGRAPHY
-  JSON
-  STRUCT
-  TIME
-  TIMETZ
-  VARCHAR
-  XML
STRING

Note: Databricks does not have a standalone JSON data type. Hence, Hevo writes the JSON, STRUCT, and ARRAY values to your Destination tables as their JSON representation in a STRING column.

Handling the Decimal data type

Hevo maps a DECIMAL value with a fixed precision (P) and scale (S) to the Databricks DECIMAL data type, as DECIMAL(P,S). If the Source does not provide the precision or the scale, Hevo cannot determine an exact DECIMAL(P,S) definition and maps the value to STRING instead to prevent data loss.

Handling of Unsupported Data Types

Hevo does not directly map the Source data types to the following Databricks data types:

  • ARRAY

  • MAP

  • STRUCT

  • VARIANT

  • Any other data type not shown in the data type hierarchy above.


Destination Considerations

  • Hevo loads the DATETIME and DATETIME_TZ values into the Databricks TIMESTAMP columns in Coordinated Universal Time (UTC). Databricks converts such a value to your session time zone when you query it. Due to this, you may observe a time difference if your session uses a time zone other than UTC. For example, a Source value 2026-09-18 10:00:00 is displayed as 2026-09-18 15:30:00 in a session that uses India Standard Time (UTC+5:30).

  • Databricks does not allow a table or column name longer than 255 characters. Additionally, a Hive Metastore workspace allows a table name of up to 128 characters only. Hevo validates your table and column names against these limits instead of shortening them, so ensure that your Source object and field names remain within the limit that applies to your catalog type.

  • When creating the schema, table, and column names in Databricks, Hevo replaces any character other than an underscore, a letter, or a digit with an underscore. Due to this, two Source fields can have the same column name. For example, the Source fields order-id and order_id are created with the same column name, order_id. In this case, Hevo loads data only for the first field and skips the second one.

  • Databricks retains the small data files created during loading, along with the files that the older table versions reference. The OPTIMIZE command combines the small files, and the VACUUM command removes the unreferenced ones. Hevo does not run these commands on your Destination tables, so in a Hive Metastore workspace, you can run them periodically to maintain query performance and free up storage. In a Unity Catalog workspace, Databricks maintains the tables that Hevo creates through predictive optimization. Ensure that this feature is enabled for your account.


Revision History

Refer to the following table for the list of key updates made to this page:

Date Release Description of Change
Sep-24-2026 NA New document.

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