/dbt_klaviyo_source

Apache License 2.0Apache-2.0

Apache License dbt logo and version

Klaviyo (Source)

This package models Klaviyo data from Fivetran's connector. It uses data in the format described by this ERD.

This package enriches your Fivetran data by doing the following:

  • Adds descriptions to tables and columns that are synced using Fivetran
  • Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
  • Models staging tables, which will be used in our transform package

Models

This package contains staging models, designed to work simultaneously with our Klaviyo modeling package. The staging models:

  • Remove any rows that are soft-deleted
  • Name columns consistently across all packages:
    • Boolean fields are prefixed with is_ or has_
    • Timestamps are appended with _at
    • ID primary keys are prefixed with the name of the table. For example, a user table's ID column is renamed user_id
    • Foreign keys include the table that they refer to. For example, a project table's owner ID column is renamed owner_user_id

Installation Instructions

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

Include in your packages.yml

packages:
  - package: fivetran/klaviyo_source
    version: [">=0.3.0", "<0.4.0"]

Configuration

By default, this package looks for your Klaviyo data in the klaviyo schema of your target database. If this is not where your Klaviyo data is, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
    klaviyo_database: your_database_name
    klaviyo_schema: your_schema_name

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 union_schemas or union_databases variables:

# dbt_project.yml
...
config-version: 2
vars:
  klaviyo_source:
    union_schemas: ['klaviyo_usa','klaviyo_canada'] # use this if the data is in different schemas/datasets of the same database/project
    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.

Contributions

Don't see a model or specific metric you would have liked to be included? Notice any bugs when installing and running the package? If so, we highly encourage and welcome contributions to this package! Please create issues or open PRs against master. Check out this post on the best workflow for contributing to a package.

Database Support

This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.

Databricks Dispatch Configuration

dbt v0.20.0 introduced a new project-level dispatch configuration that enables an "override" setting for all dispatched macros. 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.

# dbt_project.yml

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

Resources: