dbt integration

dbt integration with Datafold

Prerequisites

Make sure Datafold Github integration is set up.

Tag primary keys in dbt model

Datafold needs to know which column is the primary key of the table to perform the diff. When Datafold cannot determine the primary key of the two tables to diff, and will produce an error:

When setting up the CI integration, one of the steps is proving the primary-key tag:

This tag we can use in the dbt metadata to let Datafold know which column can be used to perform the diff. Datafold supports composite primary keys, meaning that you can assign multiple columns that make up the primary key together. There are three ways of doing this, which we'll discuss next:

Metadata

The first one is setting the tag in the dbt metadata. We set the primary key tag to primary-keyso we use this in the metadata.

models:
- name: users
columns:
- name: user_id
meta:
primary-key: true

Tags

If the primary key is not found in the metadata, it will go through the tags.

models:
- name: users
columns:
- name: user_id
tags:
- primary-key

Inferred

If the primary key isn't provided explicitly, Datafold will try to assume a pk from dbt's uniqueness tests. If you have a single column uniqueness test defined, it will use this column as the PK:

models:
- name: users
columns:
- name: user_id
tests:
- unique

Also, model level uniqueness tests are used for inferring the PK:

models:
- name: sales
columns:
- name: order_no
- name: order_line
...
tests:
- unique:
column_name: "order_no || order_line"

Finally, we also support unique_combination_ofcolumns from the dbt_utils package:

models:
- name: users
columns:
- name: order_no
- name: order_line
...
tests:
- dbt_utils.unique_combination_of_columns:
combination_of_columns:
- order_no
- order_line

Keep in mind that this is a failover mechanism. If you change the uniqueness test, this will also impact the way Datafold performs the diff.

dbt metadata synchronization

Datafold integrates very well with dbt, and also has the ability to ingest the metadata provided by dbt automatically. dbt models has metadata that can be synchronized from the production branch into the Datafold catalog. When a table has metadata being synchronized using dbt, user editing is no longer permitted for that entire table. This is to ensure that there is a single source of truth.

Metadata can be applied both on a table and column level:

models:
- name: users
description: "Description of the table"
meta:
foo: bar
tags:
- pii
- abc
columns:
- name: user_id
tags:
- pk
- id
meta:
pk: true
- name: email
description: "The user's email"
tags:
- pii
meta:
type: email

There are two special meta types:

  • owner: Used to specify the owner of the table and applies the owner of the table in the catalog view

  • <pk_tag>: The tag/name that is configured to identify primary columns is not synchronized into the meta-information, but it is synchronized as a tag if it exists.

So for the above table:

  • description is synchronized into the description field of the table in the catalog.

  • The owner of the table is set to the user identified by the [email protected] field. This user must exist in Datafold with that email.

  • The foo meta information is added to the description field with the value bar

  • The tags pii and bar are applied to the table as tags.

For the columns above:

  • The column user_id has two tags applied: pk and id

  • The metadata for user_id is ignored, because it reflects the primary key tag.

  • The email column has the description applied.

  • The email column has the tag pii applied

  • The email column has extra metadata information in the description field: type with the value email.

Metadata synchronization occurs in one of two methods:

  • The meta_schedule is set for the dbt cloud integration. This will run according to the specified cron schedule, find the most recent dbt cloud production run, and synchronize the metadata from there.

  • It can also be configured to synchronize metadata whenever a push to production happens.