Concepts

Bruin has a few simple concepts that enable you to make the most out of it.

Asset

Anything that carries value that is derived from data is an asset from our perspective.

In more concrete terms, an asset can be:

• a table/view in your database
• a file in S3 / GCS
• a machine learning model
• an MS Excel/Google Sheets/Airbyte/Notion document

This abstraction enables building multi-language data pipelines that are agnostic of a database/destinations. You will primarily be interacting with assets when using Bruin.

Here's an example SQL asset:

/* @bruin

name: dashboard.hello_bq
type: bq.sql

depends:
   - hello_python

materialization:
   type: table

columns:
  - name: one
    type: integer
    description: "Just a number"
    checks:
        - name: unique
        - name: not_null
        - name: positive
        - name: accepted_values
          value: [1, 2]

@bruin */

select 1 as one
union all
select 2 as one

Building Blocks

An asset consists of two primary components:

• definition: the metadata that enables Bruin to learn about the asset and its metadata
• content: the actual query/logic that creates the asset

The details on the asset definition can be seen here.

Pipeline

A pipeline is a group of assets that are executed together in the right order. For instance, if you have an asset that ingests data from an API, and another one that creates another table from the ingested data, you have a pipeline. Asset executions occur on a pipeline level.

A pipeline is defined with a pipeline.yml file, and all the assets need to be under a folder called assets next to this file:

- my-pipeline/
  ├─ pipeline.yml
  └─ assets/
    ├─ asset1.sql
    └─ asset2.py

Here's an example pipeline.yml:

name: bruin-init
schedule: daily # relevant for Bruin Cloud deployments

default_connections:
  google_cloud_platform: "gcp"
  snowflake: "snowflake"

Pipeline Run

When you run a pipeline, you create a "pipeline run". A pipeline run contains one or more asset instances that are executed in a given time with a specific configuration.

You can run a pipeline in the folder my-pipeline with the following command:

bruin run my-pipeline

Asset Instance

An asset instance is a single execution of an asset at a given time. For instance, if you have a Python asset and you run it, Bruin creates an asset instance that executes your code.

Asset instance is an internal concept, although it is relevant to understand since actual executions are based on asset instances.

You can run an asset with the following command:

bruin run /path/to/the/asset/file.sql

Connection

A connection is a set of credentials that enable Bruin to communicate with an external platform.

Bruin currently supports the following connection types:

• Google Cloud Platform
• Snowflake
• Postgres
• Aws Redshift
• Generic

Platform specific connections have specific schemas, and "generic" connections are built as key-value pairs to inject secrets into your assets from outside.

Connections are defined in the .bruin.yml file locally. A connection has a name and the credentials.

Default Connections

Default connections are top-level defaults that reduces repetition by stating what connections to use on types of assets. For instance, a pipeline might have SQL queries that run on Google BigQuery or Snowflake, and based on the type of an asset Bruin picks the appropriate connection.