Klaviyo Source dbt Package (Docs)

What does this dbt package do?

Materializes Klaviyo staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Klaviyo data from Fivetran's connector for analysis by doing the following:
- Names columns for consistency across all packages and for easier analysis
- Adds freshness tests to source data
- Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
Generates a comprehensive data dictionary of your klaviyo data through the dbt docs site.
These tables are designed to work simultaneously with our Klaviyo transformation package.

How do I use the dbt package?

Step 1: Prerequisites

To use this dbt package, you must have the following:

At least one Fivetran Klaviyo connector syncing data into your destination.
A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

Databricks Dispatch Configuration

If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Step 2: Install the package

Include the following klaviyo_source package version in your packages.yml file.

TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.

packages:
  - package: fivetran/klaviyo_source
    version: [">=0.7.0", "<0.8.0"]

Step 3: Define database and schema variables

By default, this package runs using your destination and the klaviyo schema. If this is not where your Klaviyo data is (for example, if your Klaviyo schema is named klaviyo_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    klaviyo_database: your_database_name
    klaviyo_schema: your_schema_name

(Optional) Step 4: Additional configurations

Expand for configurations

Unioning Multiple Klaviyo Connectors

If you have multiple Klaviyo connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the source_relation column of each model. To use this functionality, you will need to set either (note that you cannot use both) the klaviyo_union_schemas or klaviyo_union_databases variables:

# dbt_project.yml
...
config-version: 2
vars:
  klaviyo_source:
    klaviyo_union_schemas: ['klaviyo_usa','klaviyo_canada'] # use this if the data is in different schemas/datasets of the same database/project
    klaviyo_union_databases: ['klaviyo_usa','klaviyo_canada'] # use this if the data is in different databases/projects but uses the same schema name

Passthrough Columns

Additionally, this package includes all source columns defined in the macros folder. We highly recommend including custom fields in this package as models now only bring in the standard fields for the EVENT and PERSON tables. You can add more columns using our passthrough column variables. These variables allow the passthrough fields to be aliased (alias) and casted (transform_sql) if desired, although it is not required. Datatype casting is configured via a SQL snippet within the transform_sql key. You may add the desired SQL snippet while omitting the as field_name part of the casting statement - this will be dealt with by the alias attribute - and your custom passthrough fields will be casted accordingly.

Use the following format for declaring the respective passthrough variables:

# dbt_project.yml

...
vars:

  klaviyo__event_pass_through_columns:
    - name:           "property_field_id"
      alias:          "new_name_for_this_field_id"
      transform_sql:  "cast(new_name_for_this_field as int64)"
    - name:           "this_other_field"
      transform_sql:  "cast(this_other_field as string)"
  klaviyo__person_pass_through_columns:
    - name:           "custom_crazy_field_name"
      alias:          "normal_field_name"

Changing the Build Schema

By default, this package will build the Klaviyo staging models within a schema titled (<target_schema> + _stg_klaviyo) in your target database. If this is not where you would like your Klaviyo staging data to be written to, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
models:
    klaviyo_source:
        +schema: my_new_schema_name # leave blank for just the target_schema

Note that if your profile does not have permissions to create schemas in your warehouse, you can set the +schema to blank. The package will then write all tables to your pre-existing target schema.

Change the source table references

If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:

IMPORTANT: See this project's dbt_project.yml variable declarations to see the expected names.

vars:
    klaviyo_<default_source_table_name>_identifier: your_table_name

(Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for more details

Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core™ setup guides.

Does this package have dependencies?

This dbt package is dependent on the following dbt packages. These dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.

IMPORTANT: If you have any of these dependent packages in your own packages.yml file, we highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

    - package: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

How is this package maintained and can I contribute?

Package Maintenance

The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend that you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.

Contributions

A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.

We highly encourage and welcome contributions to this package. Check out this dbt Discourse article to learn how to contribute to a dbt package.

Are there any resources available?

If you have questions or want to reach out for help, see the GitHub Issue section to find the right avenue of support for you.
If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.