Twitter Organic Source dbt Package (Docs)

What does this dbt package do?

Materializes Twitter Organic staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Twitter Organic 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 Twitter Organic data through the dbt docs site.
This package contains staging models, designed to work simultaneously with our Twitter Organic transform package and our Social Media Reporting package.

How do I use the dbt package?

Step 1: Prerequisites

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

A Fivetran Twitter Organic connector syncing data into your destination.
A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

Databricks Additional 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 root 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 (skip if also using the `twitter_organic` transformation package)

Include the following twitter_organic_source package version in your packages.yml file only if you are NOT also installing the Twitter Organic transformation package. The transform package has a dependency on this source package.

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

packages:
  - package: fivetran/twitter_organic_source
    version: [">=0.3.0", "<0.4.0"] # we recommend using ranges to capture non-breaking changes automatically

Step 3: Define database and schema variables

By default, this package will look for your Twitter Organic data in the twitter schema of your target database. If this is not where your Twitter Organic data is, please add the following configuration to your dbt_project.yml file:

vars:
    twitter_organic_schema: your_schema_name
    twitter_organic_database: your_database_name

(Optional) Step 4: Additional Configurations

Expand for configurations

Changing the Build Schema

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

models:
    twitter_organic_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 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:
    twitter_<default_source_table_name>_identifier: your_table_name

Unioning Multiple Twitter Organic Connectors

If you have multiple Twitter Organic 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(s) into the final models. You will be able to see which source it came from in the source_relation column(s) 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:
    ##You may set EITHER the schemas variables below
    twitter_organic_union_schemas: ['twitter_organic_one','twitter_organic_two']

    ##OR you may set EITHER the databases variables below
    twitter_organic_union_databases: ['twitter_organic_one','twitter_organic_two']

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

Expand for details

Fivetran offers the ability for you to orchestrate your dbt project through the Fivetran Transformations for dbt Core™ product. Refer to the linked docs for more information on how to setup your project for orchestration through Fivetran.

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 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

These dbt packages are developed by a small team of analytics engineers at Fivetran. However, the packages are made better by community contributions.

We highly encourage and welcome contributions to this package. Check out this post on the best workflow for contributing to a package.

Are there any resources available?

If you encounter any 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 future dbt package to be developed, then feel free to fill out our Feedback Form.