dbt-labs/dbt-core v0.2.3.0

dbt version 0.2.3.0

on GitHub

dbt v0.2.3.0

Version 0.2.3.0 of dbt comes with the following updates:

1. Fix: Flip referential integrity arguments (breaking)

Referential integrity validations in a schema.yml file were previously defined relative to the parent table:

account:
  constraints:
    relationships:
      - {from: id, to: people, field: account_id}

Now, these validations are specified relative to the child table

people:
  constraints:
    relationships:
      - {from: account_id, to: accounts, field: id}

For more information, run dbt test -h

2. Feature: seed tables from a CSV

Previously, auxiliary data needed to be shoehorned into a view comprised of union statements, eg.

select 22 as "type", 'Chat Transcript' as type_name, 'chatted via olark' as event_name union all
select 21, 'Custom Redirect', 'clicked a custom redirect' union all
select 6, 'Email', 'email sent' union all
...

That's not a scalable solution. Now you can load CSV files into your data warehouse:

1. Add a CSV file (with a header) to the data/ directory
2. Run dbt seed to create a table from the CSV file!
3. The table name with be the filename (sans .csv) and it will be placed in your run-target's schema

Subsequent calls to dbt seed will truncate the seeded tables (if they exist) and re-insert the data. If the table schema changes, you can run dbt seed --drop-existing to drop the table and recreate it.

For more information, run dbt seed -h

3. Feature: compile analytical queries

Versioning your SQL models with dbt is a great practice, but did you know that you can also version your analyses? Any SQL files in the analysis/ dir will be compiled (ie. table names will be interpolated) and placed in the target/build-analysis/ directory. These analytical queries will not be run against your data warehouse with dbt run -- you should copy/paste them into the data analysis tool of your choice.

4. Feature: accepted values validation

In your schema.yml file, you can now add accepted-values validations:

accounts:
  constraints:
    accepted-values:
      - {field: type, values: ['paid', 'free']}

This test will determine how many records in the accounts model have a type other than paid or free.

5. Feature: switch profiles and targets on the command line

Switch between profiles with --profile [profile-name] and switch between run-targets with --target [target-name].

Targets should be something like "prod" or "dev" and profiles should be something like "my-org" or "my-side-project"

side-project:
  outputs:
    prod:
      type: redshift
      host: localhost
      port: 5439
      user: Drew
      pass:
      dbname: data_generator
      schema: ac_drew
    dev:
      type: redshift
      host: localhost
      port: 5439
      user: Drew
      pass:
      dbname: data_generator
      schema: ac_drew_dev
  run-target: dev

To compile models using the dev environment of my side-project profile:
$ dbt compile --profile side-project --target dev
or for prod:
$ dbt compile --profile side-project --target prod

You can also add a "profile' config to the dbt_config.yml file to fix a dbt project to a specific profile:

...
test-paths: ["test"]
data-paths: ["data"]

# Fix this project to the "side-project" profile
# You can still use --target to switch between environments!
profile: "side-project"

model-defaults:
....