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 up to a configured Update Delay (see the endpoint configuration below), with a default value of 30 minutes. If your SQL warehouse is configured to have an Auto Stop of more than 15 minutes, we disable the automatic delay since the delay is not as effective in saving costs with a long Auto Stop idle period.

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
/advanced	Advanced	Options for advanced users. You should not typically need to modify these.	object
/advanced/updateDelay	Update Delay	Potentially reduce active warehouse time by increasing the delay between updates. Defaults to 30 minutes if unset.	string	30m

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}

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}

Update Delay

The Update Delay parameter in Estuary materializations offers a flexible approach to data ingestion scheduling. This advanced option allows users to control when the materialization or capture tasks pull in new data by specifying a delay period. By incorporating an update delay into your workflow, you can effectively manage and optimize your active warehouse time, leading to potentially lower costs and more efficient data processing.

An update delay is configured in the advanced settings of a materialization's configuration. It represents the amount of time the system will wait before it begins materializing the latest data. This delay is specified in hours and can be adjusted according to the needs of your data pipeline.

For example, if an update delay is set to 2 hours, the materialization task will pause for 2 hours before processing the latest available data. This delay ensures that data is not pulled in immediately after it becomes available, allowing for batching and other optimizations that can reduce warehouse load and processing time.

To configure an update delay, navigate the Advanced Options section of the materialization's configuration and select a value from the drop down. The default value for the update delay in Estuary materializations is set to 30 minutes.

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