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
This package contains staging models, designed to work simultaneously with our Pendo transformation package. The staging models:
- Remove any rows that are soft-deleted
- Name columns consistently across all packages:
- Boolean fields are prefixed with
- Timestamps are appended with
- ID primary keys are prefixed with the name of the table. For example, a user table’s ID column is renamed
- Boolean fields are prefixed with
Include in your
packages: - package: fivetran/pendo_source version: [">=0.2.0", "<0.3.0"]
The Fivetran team maintaining this package only maintains the latest version. We highly recommend you keep your
packages.yml updated with the dbt hub latest version. You may refer to the CHANGELOG and release notes for more information on changes across versions.
By default, this package looks for your Pendo data in the
pendo schema of your target database. If this is not where your Pendo data is, add the following configuration to your
... config-version: 2 vars: pendo_database: your_database_name pendo_schema: your_schema_name
This package includes all of the source columns that are defined in the macros folder. We recommend including custom columns in this package because the staging models only bring in the standard columns for the
You can add more columns using our passthrough column variables. These variables allow the passthrough columns to be aliased (
alias) and casted (
transform_sql) if you want, although it is not required. You can configure datatype casting by using 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 - we rename this column with the alias attribute - and your custom passthrough columns will be casted accordingly.
Use the following format for declaring the respective passthrough variables:
vars: pendo__feature_event_pass_through_columns: - name: "custom_crazy_field_name" alias: "normal_field_name" pendo__page_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)" pendo__event_pass_through_columns: - name: "well_named_field_1" pendo__account_history_pass_through_columns: - name: "well_named_field_2" pendo__visitor_history_pass_through_columns: - name: "well_named_field_3"
Changing the Build Schemalink
By default, this package builds the Pendo staging models within a schema titled (
_stg_pendo) in your target database. If this is not where you would like your Pendo staging data to be written to, add the following configuration to your
... models: pendo_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
+schemato blank. The package will then write all tables to your pre-existing target schema.
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.
This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.
- Provide feedback on our existing dbt packages or what you’d like to see next
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- Find all of Fivetran’s pre-built dbt packages in our dbt hub
- Learn how to orchestrate your models with Fivetran Transformations for dbt Core™
- Learn more about Fivetran overall in our docs
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