Google BigQuery

Google BigQuery is a fully-managed, serverless data platform that enables super-fast SQL queries using the processing power of Google's infrastructure.

Bruin supports BigQuery as a data platform.

Connection

Google BigQuery requires a Google Cloud Platform connection, which can be added as a configuration item to connections in the .bruin.yml file complying with the following schema:

    connections:
      google_cloud_platform:
        - name: "connection_name"
          project_id: "project-id"
          
          # you can either specify a path to the service account file
          service_account_file: "path/to/file.json"
          
          # or you can specify the service account json directly
          service_account_json: |
            {
              "type": "service_account",
              ...
            }

BigQuery Assets

bq.sql

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

Example: Create a table using table materialization

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

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

Example: Run a BigQuery script

/* @bruin
name: events.install
type: bq.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)

bq.sensor.table

DANGER

BigQuery sensors are not supported yet in Bruin CLI, and they only work on Bruin Cloud.

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

Checks if a table exists in BigQuery, runs every 5 minutes until this table is available.

name: string
type: string
parameters:
    table: string

Parameters:

• table: project-id.dataset_id.table_id format, requires all of the identifiers as a full name.

Examples

# Google Analytics Events that checks if the recent date table is available
name: analytics_123456789.events
type: bq.sensor.table
parameters:
    table: "your-project-id.analytics_123456789.events_{{ end_date | date_format('%Y%m%d') }}"

bq.sensor.query

DANGER

BigQuery sensors are not supported yet in Bruin CLI, and they only work on Bruin Cloud.

Checks if a query returns any results in BigQuery, runs every 5 minutes until this query returns any results.

name: string
type: string
parameters:
    query: string

Parameters:

• query: Query you expect to return any results

Example: Partitioned upstream table

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

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

bq.seed

bq.seed are a special type of assets that are used to represent are CSV-files that contain data that is prepared outside of your pipeline that will be loaded into your bigquery 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 bigquery database.

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

name: dashboard.hello
type: bq.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 BigQuery database

The examples below show how load a csv into a bigquery database.

name: dashboard.hello
type: bq.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