https://github.com/fivetran/dbt_marketo_source
Fivetran's Marketo source dbt package.
https://github.com/fivetran/dbt_marketo_source
dbt dbt-packages fivetran marketo
Last synced: about 1 year ago
JSON representation
Fivetran's Marketo source dbt package.
- Host: GitHub
- URL: https://github.com/fivetran/dbt_marketo_source
- Owner: fivetran
- License: apache-2.0
- Created: 2020-05-05T23:56:48.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2025-01-22T01:12:56.000Z (over 1 year ago)
- Last Synced: 2025-03-29T05:33:18.675Z (about 1 year ago)
- Topics: dbt, dbt-packages, fivetran, marketo
- Language: Shell
- Homepage: https://fivetran.github.io/dbt_marketo_source/
- Size: 1.38 MB
- Stars: 4
- Watchers: 39
- Forks: 7
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# Marketo Source dbt Package ([docs](https://fivetran.github.io/dbt_market_source/))
## What does this dbt package do?
- Produces staging tables that leverage Marketo data from [Fivetran's connector](https://fivetran.com/docs/applications/marketo) in the format described by [this ERD](https://fivetran.com/docs/applications/marketo#schema).
- Adds descriptions to tables and columns that are synced using Fivetran
- Models staging tables, which will be used in our transform package
- 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 source and modeled Marketo data through the [dbt docs site](https://fivetran.github.io/dbt_marketo_source/).
- These tables are designed to work simultaneously with our [Marketo transformation package](https://github.com/fivetran/dbt_marketo/).
## How do I use the dbt package?
### Step 1: Prerequisites
To use this dbt package, you must have the following:
- At least one Fivetran Marketo connection 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.
```yml
dispatch:
- macro_namespace: dbt_utils
search_order: ['spark_utils', 'dbt_utils']
```
### Step 2: Install the package (skip if also using the `Marketo` transformation package)
If you are **not** using the [Marketo transformation package](https://github.com/fivetran/dbt_marketo), include the following package version in your `packages.yml` file. If you are installing the transform package, the source package is automatically installed as a dependency.
> TIP: Check [dbt Hub](https://hub.getdbt.com/) for the latest installation instructions or [read the dbt docs](https://docs.getdbt.com/docs/package-management) for more information on installing packages.
```yml
packages:
- package: fivetran/marketo_source
version: [">=0.13.0", "<0.14.0"]
```
### Step 3: Define database and schema variables
By default, this package runs using your destination and the `marketo` schema of your [target database](https://docs.getdbt.com/docs/running-a-dbt-project/using-the-command-line-interface/configure-your-profile). If this is not where your Marketo data is (for example, if your Marketo schema is named `marketo_fivetran`), add the following configuration to your root `dbt_project.yml` file:
```yml
vars:
marketo_database: your_database_name
marketo_schema: your_schema_name
```
### Step 4: Enabling/Disabling Models
This package takes into consideration tables that may not be synced due to slowness caused by the Marketo API. By default the `campaign`, `program`, and `activity_delete_lead` tables are enabled. If you do not sync these tables, disable the related models by adding the following to your `dbt_project.yml` file:
```yml
vars:
marketo__enable_campaigns: False # Disable if Fivetran is not syncing the campaign table. Will disable the stg_marketo__program and stg_marketo__campaigns models.
marketo__enable_programs: False # Disable if Fivetran is not syncing the program table. Will disable the stg_marketo__program model.
marketo__activity_delete_lead_enabled: False # Disable if Fivetran is not syncing the activity_delete_lead table
```
### (Optional) Step 5: Additional configurations
Expand/collapse details
#### Passing Through Additional Columns
This package includes all source columns defined in the macros folder. If you would like to pass through additional columns to the staging models, add the following configurations to your `dbt_project.yml` file. These variables allow for the pass-through fields to be aliased (`alias`) and casted (`transform_sql`) if desired, but not required. Datatype casting is configured via a sql snippet within the `transform_sql` key. You may add the desired sql while omitting the `as field_name` at the end and your custom pass-though fields will be casted accordingly. Use the below format for declaring the respective pass-through variables in your root `dbt_project.yml`.
```yml
vars:
marketo__activity_send_email_passthrough_columns:
- name: "new_custom_field"
alias: "custom_field_name"
transform_sql: "cast(custom_field_name as int64)"
- name: "a_second_field"
transform_sql: "cast(a_second_field as string)"
# a similar pattern can be applied to the rest of the following variables.
marketo__program_passthrough_columns:
```
#### Changing the Build Schema
By default this package will build the Marketo staging models within a schema titled ( + `_marketo_source`) in your target database. If this is not where you would like your Marketo data to be written to, add the following configuration to your `dbt_project.yml` file:
```yml
models:
marketo_source:
+schema: my_new_schema_name # leave blank for just the target_schema
```
#### Change the source table references
If an individual source table has a different name than what 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`](https://github.com/fivetran/dbt_marketo_source/blob/main/dbt_project.yml) variable declarations to see the expected names.
```yml
vars:
marketo__identifier: "your_table_name"
```
### (Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™
Expand for details
Fivetran offers the ability for you to orchestrate your dbt project through [Fivetran Transformations for dbt Core™](https://fivetran.com/docs/transformations/dbt). Learn how to set up your project for orchestration through Fivetran in our [Transformations for dbt Core setup guides](https://fivetran.com/docs/transformations/dbt#setupguide).
## 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](https://hub.getdbt.com/) 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.
```yml
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 you stay consistent with the [latest version](https://hub.getdbt.com/fivetran/marketo/latest/) of the package and refer to the [CHANGELOG](https://github.com/fivetran/dbt_marketo_source/blob/main/CHANGELOG.md) 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](https://discourse.getdbt.com/t/contributing-to-a-dbt-package/657) on the best workflow for contributing to a package.
## Are there any resources available?
- If you have questions or want to reach out for help, see the [GitHub Issue](https://github.com/fivetran/dbt_marketo_source/issues/new/choose) 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](https://www.surveymonkey.com/r/DQ7K7WW).