Databricks

Databricks is a unified data analytics platform that provides a collaborative environment for data scientists, data engineers, and business analysts. It is built on top of Apache Spark, which makes it easy to scale and process big data workloads.

Bruin supports Databricks as a data platform.

Connection

In order to work with Databricks you can add as a configuration item to connections in the .bruin.yml file complying with the following schema:

    connections:
      databricks:
        - name: "connection_name"
          token: "your-databricks-token"
          path: "your-databricks-endpoint-path"
          host: "your-databricks-host"
          port: "your-databricks-port"
          catalog: "your-databricks-catalog"
          schema: "your-databricks-schema"

Step 1: Generate a token

Click on your Databricks username in the top bar, and then select "Settings" from the dropdown menu. Click on the "Developer" tab in the column "Settings" on the left. Next to "Access tokens," click "Manage." Click the "Generate new token" button. Enter Token Details and click "Generate".

Step 2: Retrieve HTTP path

Click on "SQL Warehouses" in the bar on the left. Select your warehouse from the list. Click on "Connection details" towards the top of the page. Then copy the HTTP path. It should look something like /sql/1.0/warehouses/3748325bf498i274

Step 3: Retrieve host

The host URL is typically visible in the browser's address bar. It should look something like: {databricks-instance}.cloud.databricks.com.

Alternatively, you can find the host URL in the workspace settings.

Step 4: Enter port, catalog and schema

Databricks APIs and SQL warehouse endpoints use 443 (HTTPS). So port will usually be 443. The catalog and schema can be found under the section "Catalog" in the bar on the left.

The Databricks configuration in .bruin.yml should like something like this:

    connections:
      databricks:
        - name: databricks-default
          token: XXXXXXXXXXXXXXX
          path: /sql/1.0/warehouses/3748325bf498i274
          host: dbc-example-host.cloud.databricks.com
          port: 443
          catalog: default
          schema: example_schema

Databricks Assets

databricks.sql

Runs a materialized Databricks asset or a Databricks SQL script. For detailed parameters, you can check Definition Schema page.

Example: Create a table using table materialization

/* @bruin
name: events.install
type: databricks.sql
materialization:
    type: table
@bruin */

select user_id, ts, platform, country
from analytics.events
where event_name = "install"

Example: Run a script

/* @bruin
name: events.install
type: databricks.sql
@bruin */

create temp table first_installs as
select 
    user_id, 
    min(ts) as install_ts,
    min_by(platform, ts) as platform,
    min_by(country, ts) as country
from analytics.events
where event_name = "install"
group by 1;

create or replace table events.install
select
    user_id, 
    i.install_ts,
    i.platform, 
    i.country,
    a.channel,
from first_installs as i
join marketing.attribution as a
    using(user_id)

databricks.sensor.query

Checks if a query returns any results in Databricks, runs by default every 30 seconds until this query returns any results.

name: string
type: string
parameters:
    query: string
    poke_interval: int (optional)

Parameters:

• query: Query you expect to return any results
• poke_interval: The interval between retries in seconds (default 30 seconds).

databricks.sensor.table

Sensors are a special type of assets that are used to wait on certain external signals.

Checks if a table exists in Databricks, runs by default every 30 seconds until this table is available.

name: string
type: string
parameters:
    table: string
    poke_interval: int (optional)

Parameters:

• table: schema_id.table_id.
• poke_interval: The interval between retries in seconds (default 30 seconds).

Example: Partitioned upstream table

Checks if the data available in upstream table for end date of the run.

name: analytics_123456789.events
type: databricks.sensor.query
parameters:
    query: select exists(select 1 from upstream_table where dt = "{{ end_date }}"

Example: Streaming upstream table

Checks if there is any data after end timestamp, by assuming that older data is not appended to the table.

name: analytics_123456789.events
type: databricks.sensor.query
parameters:
    query: select exists(select 1 from upstream_table where inserted_at > "{{ end_timestamp }}"

databricks.seed

databricks.seed is a special type of asset used to represent CSV files that contain data that is prepared outside of your pipeline that will be loaded into your Databricks database. Bruin supports seed assets natively, allowing you to simply drop a CSV file in your pipeline and ensuring the data is loaded to the Databricks database.

You can define seed assets in a file ending with .yaml:

name: dashboard.hello
type: databricks.seed

parameters:
    path: seed.csv

Parameters:

• path: The path parameter is the path to the CSV file that will be loaded into the data platform. path is relative to the asset definition file.

Examples: Load csv into a Databricks database

The examples below show how to load a CSV into a Databricks database.

name: dashboard.hello
type: databricks.seed

parameters:
    path: seed.csv

Example CSV:

name,networking_through,position,contact_date
Y,LinkedIn,SDE,2024-01-01
B,LinkedIn,SDE 2,2024-01-01