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Overview

This guide will take you through the steps to integrate SDF with an existing DBT project. SDF currently works with DBT v1.7.0 and above.

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Example

This first section will walk you through the steps to integrate SDF with an existing DBT project. We’ll use the jaffle_shop example project from DBT to demonstrate this.

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Prerequisites

Ensure that you have the following installed and configured locally before beginning.
  • DBT
  • A valid profiles.yml file configured wth DBT. See here for more information.
Next, you’ll need the dbt-core Jaffle Shop example project setup locally. You can clone it from here.
Without a valid profiles.yml, the Jaffle Shop example will not be able to compile, and SDF will not work.
We use this example as it doesn’t require authentication to a database or existing warehouse. In a production scenario, you’d likely compile DBT with your own models and remote warehouse, requiring authentication. To use SDF in this context, check our integrations page to see if we support your warehouse. If not, DDLs can be manually added to SDF alongside the DBT project to enable SDF compilation.

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Guide

1

Compile DBT

dbt compile will compile the DBT project and generate the manifest file. This must be run first before SDF can work.
dbt compile
2

Initialize the SDF DBT Workspace

Using the next command, SDF will create a workspace.sdf.yml based on your DBT project’s configuration. This file will be placed adjacent to your dbt_project.yml file and should be committed to your repository.
sdf dbt init
Notice that your workspace.sdf.yml includes block points to files within the target directory. This is because SDF deals with raw SQL directly and not DBT models. To accomplish this, SDF copies the necessary compiled DBT models into the sdf directory with target/compiled (or your configured target-path).This command will also compile DBT snapshots into a format SDF can understand, and port over credentials stored in your profiles.yml file to the ~/.sdf/credentials directory in the root of your system. This credentials will be used by SDF later to fetch required table schemas from the cloud warehouse.As new changes to models or snapshots are made, running sdf dbt refresh will refresh the sdf workspace to point to the latest.
Our YML processor does not support YML anchors nor YML aliases. If you have YML anchors or aliases in your DBT project, you may need to remove them and refactor before running sdf dbt init.
3

Configure the Integrations Block

If using DBT with a cloud warehouse, you’ll likely need to configure the integrations block in the workspace.sdf.yml file. This block will contain the necessary information to connect to your warehouse.In DBT terms, this block replaces your DBT sources. As such, it enables SDF to pull down the schema information for table dependencies not defined in SQL. For example, if I had some DBT sources coming from the database my_db in Snowflake, I would use the following configuration to pull them down at compile-time:
integrations:
	- provider: snowflake
		type: database
		sources:
			- pattern: my_db.*.*
For more information, or for guides on how to configure this for other warehouses, check out our Integrations section.
This integrations block will be generated by the sdf dbt init command in future versions. Stay tuned!
4

Compile SDF

Now that we have our workspace.sdf.yml file configured, we can compile SDF. This will validate your SQL, dependencies, and produce all SDF artifacts (including column-level lineage). These artifacts will be placed it in the sdftarget directory local to your DBT project.
sdf compile
Great, now that we’ve successfully compiled our models, let’s try adding some metadata.
5

Classify a DBT Model

In our jaffle shop example, we have a table created from a dbt seed called raw_customers. This table contains two columns (first_name, last_name) with personally identifiable information (PII). Let’s classify these columns as PII in SDF, and ensure any downstream usage of these columns also inherits this classification.First, let’s define our PII classifier in the workspace.sdf.yml file.
---
classifier: 
	name: pii
	description: Personally Identifiable Information
	labels: 
		- name: name
		  description: An individual's first, middle, or last name
Next, let’s attach this to the right columns raw_customers table in the workspace.sdf.yml file.
---
table:
	name: raw_customers
	columns:
	  - name: first_name
		  classifiers:
		  - pii.name
	  - name: last_name
		  classifiers:
		  - pii.name
Great, now let’s compile SDF again and see what happens.
sdf compile --show all

Working set 6 model files, 1 .sdf file
Finished 8 models [8 reused] in 0.089 secs

Schema jaffle_shop.dbt_alice.raw_customers
+-------------+-----------+------------+
| column_name | data_type | classifier |
+-------------+-----------+------------+
| id          | bigint    |            |
| first_name  | varchar   | pii.name   |
| last_name   | varchar   | pii.name   |
+-------------+-----------+------------+

...
...

Schema jaffle_shop.dbt_alice.stg_customers
+-------------+-----------+------------+
| column_name | data_type | classifier |
+-------------+-----------+------------+
| customer_id | bigint    |            |
| first_name  | varchar   | pii.name   |
| last_name   | varchar   | pii.name   |
+-------------+-----------+------------+

...
...

Schema jaffle_shop.dbt_alice.customers
+-------------------------+-----------+------------+
| column_name             | data_type | classifier |
+-------------------------+-----------+------------+
| customer_id             | bigint    |            |
| first_name              | varchar   | pii.name   |
| last_name               | varchar   | pii.name   |
| first_order             | date      |            |
| most_recent_order       | date      |            |
| number_of_orders        | bigint    |            |
| customer_lifetime_value | bigint    |            |
+-------------------------+-----------+------------+
You’ll notice the classification is not only attached to the raw_customers table, but also to the downstream customers table and others. This is because SDF is able to infer the lineage between these two tables and propagate the classification.

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Commands

Here we layout the sdf dbt commands available for us when developing with SDF and DBT locally.

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sdf dbt init

This command will initialize the SDF workspace for your DBT project. It will create a workspace.sdf.yml file adjacent to your dbt_project.yml file. It will also configure all DBT seeds and compile DBT snapshots into a format SDF can understand. Lastly, it will copy the compiled models into the sdf directory with target/compiled (or your configured target-path). 
sdf dbt init

Initialize a sdf workspace from a dbt project — best effortUsage: sdf dbt init [OPTIONS]Options:
      —target <TARGET>                Use this DBT target over the default target in profiles.yml
      —profiles-dir <PROFILES_DIR>    Use this DBT profile instead of the defaults at ~/.dbt/profile.yml — (note dbt uses —profile_dir, this CLI uses —profile-dir)
      —workspace-dir <WORKSPACE_DIR>  Specifies the workspace directory where we expect to see manifest and dbt project files The SDF workspace file will be placed in the same directory. Default: current directory
  -s, —save                           Save and overwrite the workspace file
  -c, —config <CONFIG>                Supply a config yml file or provide config as yml string e.g. ‘{key: value}’
      —log-level <LOG_LEVEL>          Set log level [possible values: trace, debug, debug-pretty, info, warn, error]
      —log-file <LOG_FILE>            Creates or replaces the log file
      —show-all-errors                Don’t suppress errors
  -h, —help                           Print help
dbt compile must be run before running sdf dbt init

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sdf dbt refresh

This command will refresh the SDF workspace for your DBT project. This is useful if you make changes to DBT models during development, then would like to ensure SDF works with your latest models without regenerating the workspace.sdf.yml file. It will recompile the DBT snapshots and move the compiled models into the sdf directory with target/compiled (or your configured target-path). 
sdf dbt refresh

Re-initialize a sdf workspace from a dbt project — best effortUsage: sdf dbt refresh [OPTIONS]Options:
      —target <TARGET>                Use this DBT target over the default target in profiles.yml
      —profiles-dir <PROFILES_DIR>    Use this DBT profile instead of the defaults at ~/.dbt/profile.yml — (note dbt uses —profile_dir, this CLI uses —profile-dir)
      —workspace-dir <WORKSPACE_DIR>  Specifies the workspace directory where we expect to see manifest and dbt project files The SDF workspace file will be placed in the same directory. Default: current directory
  -s, —save                           Save and overwrite the workspace file
  -c, —config <CONFIG>                Supply a config yml file or provide config as yml string e.g. ‘{key: value}’
      —log-level <LOG_LEVEL>          Set log level [possible values: trace, debug, debug-pretty, info, warn, error]
      —log-file <LOG_FILE>            Creates or replaces the log file
      —show-all-errors                Don’t suppress errors
  -h, —help                           Print help
Sometimes, updates are required to the workspace.sdf.yml to work with the latest DBT models. For example, maybe you’ve added your first snapshot, requiring SDF to add a new includes path to the workspace.sdf.yml file. By default, sdf dbt refresh will not make necessary changes to your workspace.sdf.yml in order to reflect any updates to your DBT project. However, you can use the --save flag to save these changes to the file. 
sdf dbt refresh --save
Auto-updates to the workspace.sdf.yml are best effort and may result in unintended updates or reformatting.

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Conclusion

SDF can work alongside an existent DBT project to power column-level lineage, SQL compilation and validation, impact analysis, data classification, and much more for DBT models. This integration is actively under development, with lots more coming soon.