Skip to content

Latest commit

 

History

History
250 lines (206 loc) · 14.8 KB

README.md

File metadata and controls

250 lines (206 loc) · 14.8 KB
Raw

Stripe Source dbt Package (Docs)

What does this dbt package do?

  • Materializes Stripe staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Stripe 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 Stripe data through the dbt docs site.
  • These tables are designed to work simultaneously with our Stripe transformation package.

How do I use the dbt package?

Step 1: Prerequisites

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

  • At least one Fivetran stripe connector syncing data into your destination.
  • A BigQuery, Snowflake, Redshift, Databricks, or PostgreSQL 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.

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

Step 2: Install the package (skip if also using the stripe transformation package)

If you are not using the Stripe 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/stripe_source
    version: [">=0.12.0", "<0.13.0"]

Step 3: Define database and schema variables

By default, this package runs using your destination and the stripe schema. If this is not where your stripe data is (for example, if your stripe schema is named stripe_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    stripe_database: your_destination_name
    stripe_schema: your_schema_name

Step 4: Disable models for non-existent sources

This package takes into consideration that not every Stripe account utilizes the invoice, invoice_line_item, payment_method, payment_method_card, plan, price, subscription, product, or credit_note features, and allows you to disable the corresponding functionality. By default, all variables' values are assumed to be true with the exception of credit_note. Add variables for only the tables you want to disable or enable respectively:

# dbt_project.yml

...
vars:
    stripe__using_invoices:        False  #Disable if you are not using the invoice and invoice_line_item tables
    stripe__using_payment_method:  False  #Disable if you are not using the payment_method and payment_method_card tables
    stripe__using_subscriptions:   False  #Disable if you are not using the subscription, product, and plan/price tables.
    stripe__using_credit_notes:    True   #Enable if you are using the credit note tables.

(Optional) Step 5: Additional configurations

Expand to view configurations

Unioning Multiple Stripe Connectors

If you have multiple Stripe connectors you would like to use this package on simultaneously, we have added the ability to do so. Data from disparate connectors will be unioned together and be passed downstream to the end models. The source_relation column will specify where each record comes from. To use this functionality, you will need to either set the stripe_union_schemas or stripe_union_databases variables. Please also make sure the single-source stripe_database and stripe_schema variables are removed.

# dbt_project.yml

...
config-version: 2

vars:
    stripe_union_schemas: ['stripe_us','stripe_mx'] # use this if the data is in different schemas/datasets of the same database/project
    stripe_union_databases: ['stripe_db_1','stripe_db_2'] # use this if the data is in different databases/projects but uses the same schema name

Leveraging Plan vs Price Sources

Customers using Fivetran with the newer Stripe Price API will have a price table, and possibly a plan table if that was used previously. Therefore to accommodate two different source tables we added logic to check if there exists a price table by default. If not, it will leverage the plan table. However if you wish to use the plan table instead, you may set stripe__using_price to false in your dbt_project.yml to override the macro.

# dbt_project.yml

...
config-version: 2

vars:
  stripe__using_price: false #  True by default. If true, will look `price ` table. If false, will look for the `plan` table.

Leveraging Subscription Vs Subscription History Sources

For Stripe connectors set up after February 09, 2022 the subscription table has been replaced with the new subscription_history table. By default this package will look for your subscription data within the subscription_history source table. However, if you have an older connector then you must configure the stripe__using_subscription_history to false in order to have the package use the subscription source rather than the subscription_history table.

Please note that if you have stripe__using_subscription_history enabled then the package will filter for only active records.

vars:
    stripe__using_subscription_history: False  # True by default. Set to False if your connector syncs the `subscription` table instead.

Running on Live vs Test Customers

By default, this package will run on non-test data (where livemode = true) from the source Stripe tables. However, you may want to include and focus on test data when testing out the package or developing your analyses. To run on only test data, add the following configuration to your root dbt_project.yml file:

vars:
    stripe_source:
        stripe__using_livemode: false  # Default = true

Including sub Invoice Line Items

By default, this package will filter out any records from the invoice_line_item source table which include the string sub_. This is due to a legacy Stripe issue where sub_ records were found to be duplicated. However, if you highly utilize these records you may wish they be included in the final output of the stg_stripe__invoice_line_item model. To do, so you may include the below variable configuration in your root dbt_project.yml:

vars:
    stripe_source:
        stripe__using_invoice_line_sub_filter: false # Default = true

Pivoting out Metadata Properties

Oftentimes you may have custom fields within your source tables that is stored as a JSON object that you wish to pass through. By leveraging the metadata variable, this package pivot out fields into their own columns. The metadata variables accept dictionaries in addition to strings.

Additionally, you may alias your field if you happen to be using a reserved word as a metadata field, any otherwise incompatible name, or just wish to rename your field. Below are examples of how you would add the respective fields.

The metadata JSON field is present within the customer, charge, card, dispute, invoice, invoice_line_item, payment_intent, payment_method, payout, plan, price, refund, subscription, and transfer source tables. To pivot these fields out and include in the respective downstream staging model, add the respective variable(s) to your root dbt_project.yml file like below.

vars: 
  stripe__account_metadata:
    - name: metadata_field
    - name: another_metadata_field
    - name: and_another_metadata_field
  stripe__charge_metadata:
    - name: metadata_field_1
  stripe__card_metadata:
    - name: metadata_field_10
  stripe__customer_metadata:
    - name: metadata_field_6
      alias: metadata_field_six
  stripe__dispute_metadata:
    - name: dispute_metadata_field
  stripe__invoice_metadata: 
    - name: metadata_field_2
  stripe__invoice_line_item_metadata: 
    - name: metadata_field_20
  stripe__payment_intent_metadata:
    - name: incompatible.field
      alias: rename_incompatible_field
  stripe__payment_method_metadata:
    - name: field_is_reserved_word
      alias: field_is_reserved_word_xyz
  stripe__payout_metadata:
    - name: 123
      alias: one_two_three
  stripe__price_plan_metadata: ## Used for both Price and Plan sources
    - name: rename_price
      alias: renamed_field_price
  stripe__refund_metadata:
    - name: metadata_field_3
  stripe__subscription_metadata:
    - name: 567
      alias: five_six_seven
  stripe__transfer_metadata:
    - name: transfer_metadata_field

Alternatively, if you only have strings in your JSON object, the metadata variable accepts the following configuration as well.

vars:
    stripe__subscription_metadata: ['the', 'list', 'of', 'property', 'fields'] # Note: this is case-SENSITIVE and must match the casing of the property as it appears in the JSON

Passing Through Additional Fields

This package includes all source columns defined in the macros folder. You can add more columns using our pass-through column variables. 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:

# dbt_project.yml

vars:
  stripe_source:
    card_pass_through_columns:
      - name: "description"
      - name: "iin"
      - name: "issuer"
        alias: "card_issuer"  # optional: define an alias for the column 
        transform_sql: "cast(card_issuer as string)" # optional: apply transformation to column. must reference the alias if provided

Change the build schema

By default, this package builds the stripe staging models within a schema titled (<target_schema> + _stg_stripe) in your destination. If this is not where you would like your stripe staging data to be written to, add the following configuration to your root dbt_project.yml file:

models:
    stripe_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:
    stripe_<default_source_table_name>_identifier: your_table_name

(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™

Expand to view 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.

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 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.

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 to learn how to contribute to a dbt package.

Are there any resources available?

  • If you have 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 new dbt package, fill out our Feedback Form.