Snapchat Ads Source dbt Package (Docs)
- Materializes Snapchat Ads staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Snapchat Ads data from Fivetran's connector for analysis by doing the following:
- Name 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 Snapchat Ads data through the dbt docs site.
- These tables are designed to work simultaneously with our Snapchat Ads transformation package.
- Refer to our Docs site for more details about these materialized models.
To use this dbt package, you must have the following:
- At least one Fivetran Snapchat Ads connector syncing data into your destination.
- A BigQuery, Snowflake, Redshift, Databricks, or PostgreSQL destination.
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']If you are not using the Snapchat Ads transformation package, 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 for the latest installation instructions or read the dbt docs for more information on installing packages.
packages:
- package: fivetran/snapchat_ads_source
version: [">=0.6.0", "<0.7.0"] # we recommend using ranges to capture non-breaking changes automaticallyBy default, this package runs using your destination and the snapchat_ads_source schema. If this is not where your Snapchat Ads data is (for example, if your Snapchat Ads schema is named snapchat_ads_fivetran), you would add the following configuration to your root dbt_project.yml file with your custom database and schema names:
vars:
snapchat_ads_database: your_destination_name
snapchat_ads_schema: your_schema_name If you have multiple snapchat_ads 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 the snapchat_ads_union_schemas OR snapchat_ads_union_databases variables (cannot do both) in your root dbt_project.yml file:
vars:
snapchat_ads_union_schemas: ['snapchat_ads_usa','snapchat_ads_canada'] # use this if the data is in different schemas/datasets of the same database/project
snapchat_ads_union_databases: ['snapchat_ads_usa','snapchat_ads_canada'] # use this if the data is in different databases/projects but uses the same schema namePlease be aware that the native source.yml connection set up in the package will not function when the union schema/database feature is utilized. Although the data will be correctly combined, you will not observe the sources linked to the package models in the Directed Acyclic Graph (DAG). This happens because the package includes only one defined source.yml.
To connect your multiple schema/database sources to the package models, follow the steps outlined in the Union Data Defined Sources Configuration section of the Fivetran Utils documentation for the union_data macro. This will ensure a proper configuration and correct visualization of connections in the DAG.
By default, this package will select clicks, impressions, and cost from the source reporting tables to store into the staging models. If you would like to pass through additional metrics to the staging models, add the below configurations to your dbt_project.yml file. These variables allow for the pass-through fields to be aliased (alias) if desired, but not required. Use the below format for declaring the respective pass-through variables:
Note Please ensure you exercised due diligence when adding metrics to these models. The metrics added by default (taps, impressions, and spend) have been vetted by the Fivetran team maintaining this package for accuracy. There are metrics included within the source reports, for example metric averages, which may be inaccurately represented at the grain for reports created in this package. You will want to ensure whichever metrics you pass through are indeed appropriate to aggregate at the respective reporting levels provided in this package.
vars:
snapchat_ads__ad_hourly_passthrough_metrics:
- name: "new_custom_field"
alias: "custom_field"
snapchat_ads__ad_squad_hourly_passthrough_metrics:
- name: "this_field"
snapchat_ads__campaign_hourly_report_passthrough_metrics:
- name: "unique_string_field"
alias: "field_id"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.ymlvariable declarations to see the expected names.
vars:
snapchat_ads_<default_source_table_name>_identifier: your_table_name By default, this package builds the Snapchat Ads staging models within a schema titled (<target_schema> + _stg_snapchat_ads) in your destination. If this is not where you would like your Snapchat Ads staging data to be written to, add the following configuration to your root dbt_project.yml file:
models:
snapchat_ads_source:
+schema: my_new_schema_name # leave blank for just the target_schemaExpand 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.
This dbt package is dependent on the following dbt packages. Please be aware that 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.ymlfile, we highly recommend that you remove them from your rootpackages.ymlto 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"]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.
In creating this package, which is meant for a wide range of use cases, we had to take opinionated stances on a few different questions we came across during development. We've consolidated significant choices we made in the DECISIONLOG.md, and will continue to update as the package evolves. We are always open to and encourage feedback on these choices, and the package in general.
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!
- If you have questions or want to reach out for help, please refer to 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.
- Have questions or want to just say hi? Book a time during our office hours on Calendly or email us at [email protected].
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