Databricks

This connector materializes Flow collections into tables in a Databricks SQL Warehouse. It allows both standard and delta updates.

The connector first uploads data changes to a Databricks Unity Catalog Volume. From there, it transactionally applies the changes to the Databricks tables.

ghcr.io/estuary/materialize-databricks:dev provides the latest connector image. You can also follow the link in your browser to see past image versions.

Prerequisites

To use this connector, you'll need:

• A Databricks account that includes:
  • A unity catalog
  • A SQL Warehouse
  • A schema — a logical grouping of tables in a catalog
  • A user with a role assigned that grants the appropriate access levels to these resources.
• At least one Flow collection

tip

If you haven't yet captured your data from its external source, start at the beginning of the guide to create a dataflow. You'll be referred back to this connector-specific documentation at the appropriate steps.

Setup

You need to first create a SQL Warehouse if you don't already have one in your account. See Databricks documentation on configuring a Databricks SQL Warehouse. After creating a SQL Warehouse, you can find the details necessary for connecting to it under the Connection Details tab.

In order to save on costs, we recommend that you set the Auto Stop parameter for your SQL warehouse to the minimum available. Estuary's Databricks connector automatically delays updates to the destination according to the configured Sync Schedule (see configuration details below), with a default delay value of 30 minutes.

You also need an access token for your user to be used by our connector, see the respective documentation from Databricks on how to create an access token.

Configuration

To use this connector, begin with data in one or more Flow collections. Use the below properties to configure a Databricks materialization, which will direct one or more of your Flow collections to new Databricks tables.

Properties

Endpoint

Property	Title	Description	Type	Required/Default
/address	Address	Host and port of the SQL warehouse (in the form of host[:port]). Port 443 is used as the default if no specific port is provided.	string	Required
/http_path	HTTP Path	HTTP path of your SQL warehouse	string	Required
/catalog_name	Catalog Name	Name of your Unity Catalog	string	Required
/schema_name	Schema Name	Default schema to materialize to	string	default schema is used
/credentials	Credentials	Authentication credentials	object
/credentials/auth_type	Role	Authentication type, set to PAT for personal access token	string	Required
/credentials/personal_access_token	Role	Personal Access Token	string	Required

Bindings

Property	Title	Description	Type	Required/Default
/table	Table	Table name	string	Required
/schema	Alternative Schema	Alternative schema for this table	string	Required
/delta_updates	Delta updates	Whether to use standard or delta updates	boolean	false

Sample

materializations:
  ${PREFIX}/${mat_name}:
    endpoint:
  	    connector:
    	    config:
              address: dbc-abcdefgh-a12b.cloud.databricks.com
              catalog_name: main
              http_path: /sql/1.0/warehouses/abcd123efgh4567
              schema_name: default
              credentials:
                auth_type: PAT
                personal_access_token: secret
    	    image: ghcr.io/estuary/materialize-databricks:dev
  # If you have multiple collections you need to materialize, add a binding for each one
    # to ensure complete data flow-through
    bindings:
  	- resource:
      	table: ${table_name}
        schema: default
    source: ${PREFIX}/${source_collection}

Sync Schedule

This connector supports configuring a schedule for sync frequency. You can read about how to configure this here.

Delta updates

This connector supports both standard (merge) and delta updates. The default is to use standard updates.

Enabling delta updates will prevent Flow from querying for documents in your Databricks table, which can reduce latency and costs for large datasets. If you're certain that all events will have unique keys, enabling delta updates is a simple way to improve performance with no effect on the output. However, enabling delta updates is not suitable for all workflows, as the resulting table in Databricks won't be fully reduced.

You can enable delta updates on a per-binding basis:

    bindings:
  	- resource:
      	table: ${table_name}
        schema: default
        delta_updates: true
    source: ${PREFIX}/${source_collection}

Reserved words

Databricks has a list of reserved words that must be quoted in order to be used as an identifier. Flow automatically quotes fields that are in the reserved words list. You can find this list in Databricks's documentation here and in the table below.

caution

In Databricks, objects created with quoted identifiers must always be referenced exactly as created, including the quotes. Otherwise, SQL statements and queries can result in errors. See the Databricks docs.

Reserved words
ANTI
EXCEPT	FULL
INNER	INTERSECT
JOIN	LATERAL
LEFT	MINUS
NATURAL	ON
RIGHT	SEMI
SEMI	USING
NULL	DEFAULT
TRUE	FALSE
CROSS