- Materializes Netsuite staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Netsuite 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 netsuite data through the dbt docs site.
- These tables are designed to work simultaneously with our Netsuite transformation package.
Step 1: Prerequisiteslink
To use this dbt package, you must have At least either one Fivetran Netsuite (netsuite.com) or Netsuite2 (netsuite2) connector syncing the respective tables to your destination:
This package is compatible with either a BigQuery, Snowflake, Redshift, or PostgreSQL destination.
Step 2: Install the packagelink
Include the following netsuite_source package version in your
packages: - package: fivetran/netsuite_source version: [">=0.5.0", "<0.6.0"]
Step 3: Define Netsuite.com or Netsuite2 Source
As of April 2022 Fivetran made available a new Netsuite connector which leverages the Netsuite2 endpoint opposed to the original Netsuite.com endpoint. This package is designed to run for either or, not both. By default the
netsuite_data_model variable for this package is set to the original
netsuite value which runs the netsuite.com version of the package. If you would like to run the package on Netsuite2 data, you may adjust the
netsuite_data_model variable to run the
netsuite2 version of the package.
vars: netsuite_data_model: netsuite2
Step 4: Define database and schema variableslink
By default, this package runs using your destination and the
netsuite schema. If this is not where your Netsuite data is (for example, if your netsuite schema is named
netsuite_fivetran), add the following configuration to your root
vars: netsuite_database: your_destination_name netsuite_schema: your_schema_name
(Optional) Step 5: Additional configurationslink
Passing Through Additional Fieldslink
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:
vars: accounts_pass_through_columns: - name: "new_custom_field" alias: "custom_field" classes_pass_through_columns: - name: "this_field" departments_pass_through_columns: - name: "unique_string_field" alias: "field_id" transform_sql: "cast(field_id as string)" transactions_pass_through_columns: - name: "that_field" transaction_lines_pass_through_columns: - name: "other_id" alias: "another_id" transform_sql: "cast(another_id as int64)" customers_pass_through_columns: - name: "customer_custom_field" alias: "customer_field" locations_pass_through_columns: - name: "location_custom_field" subsidiaries_pass_through_columns: - name: "sub_field" alias: "subsidiary_field" consolidated_exchange_rates_pass_through_columns: - name: "consolidate_this_field"
Change the build schemalink
By default, this package builds the Netsuite staging models within a schema titled (
_netsuite_source) in your destination. If this is not where you would like your netsuite staging data to be written to, add the following configuration to your root
models: netsuite_source: +schema: my_new_schema_name # leave blank for just the target_schema
Change the source table referenceslink
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: # For all Netsuite source tables netsuite_<default_source_table_name>_identifier: your_table_name # For all Netsuite2 source tables netsuite2_<default_source_table_name>_identifier: your_table_name
Override the data models variablelink
This package is designed to run either the Netsuite.com or Netsuite2 data models. However, for documentation purposes, an additional variable
netsuite_data_model_override was created to allow for both data model types to be run at the same time by setting the variable value to
netsuite. This is only to ensure the dbt docs (which is hosted on this repository) is generated for both model types. While this variable is provided, we recommend you do not adjust the variable and instead change the
netsuite_data_model variable to fit your configuration needs.
(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™link
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 root
packages.ymlto avoid package version conflicts.
packages: - package: fivetran/fivetran_utils version: [">=0.3.0", "<0.4.0"] - package: dbt-labs/dbt_utils version: [">=0.8.0", "<0.9.0"]
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.
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@example.com.