Netsuite Transformation dbt Package (Docs)
- Produces modeled tables that leverage Netsuite data from Fivetran's connector in the format described by this ERD and builds off the output of our Netsuite source package.
- Enables users to insights into their netsuite data that can be used for financial statement reporting and deeper transactional analysis. This is achieved by the following:
- Recreating both the balance sheet and income statement
- Recreating commonly used data by using the transaction lines as the base table and joining other data
- Generates a comprehensive data dictionary of your source and modeled Netsuite data through the dbt docs site.
The following table provides a detailed list of all tables materialized within this package by default.
TIP: See more details about these tables in the package's dbt docs site.
| Table | Description |
|---|---|
| netsuite__transaction_details or netsuite2__transaction_details | All transactions with the associated accounting period, account and subsidiary information. Where applicable, you can also see data about the customer, location, item, vendor, and department. |
| netsuite__income_statement or netsuite2__income_statement | All transaction lines necessary to generate an income statement (converted for the appropriate exchange rate of the parent subsidiary). Department, class, and location information are included for additional reporting functionality. |
| netsuite__balance_sheet or netsuite2__balance_sheet | All transaction lines necessary to generate a balance sheet (converted for the appropriate exchange rate of the parent subsidiary). Non balance sheet transactions are categorized as either Retained Earnings or Net Income. |
Many of the above reports are now configurable for visualization via Streamlit! Check out some sample reports here.
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:
- accounts
- accounting_periods
- accounting_books
- consolidated_exchange_rates
- currencies
- customers
- classes
- departments
- expense_accounts
- income_accounts
- items
- locations
- partners
- transaction_lines
- transactions
- subsidiaries
- vendors
- vendor_types
- account
- accounttype
- accountingbooksubsidiary
- accountingperiodfiscalcalendar
- accountingperiod
- accountingbook
- consolidatedexchangerate
- currency
- customer
- classification
- department
- entity
- entityaddress
- item
- job
- location
- locationmainaddress
- transactionaccountingline
- transactionline
- transaction
- subsidiary
- vendor
- vendorcategory
This package is compatible with either a BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
If you are using a Databricks destination with this package, you must add the following (or a variation of the following) 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']Do NOT include the netsuite_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.
Include the following netsuite package version in your packages.yml file:
TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.
packages:
- package: fivetran/netsuite
version: [">=0.15.0", "<0.16.0"]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 #netsuite by defaultBy 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 dbt_project.yml file:
vars:
netsuite_database: your_destination_name
netsuite_schema: your_schema_name It's possible that your Netsuite connector does not sync every table that this package expects. If your syncs exclude certain tables, it is because you either don't use that feature in Netsuite or actively excluded some tables from your syncs. To disable the corresponding functionality in the package, you must add the relevant variables. By default, all variables are assumed to be true. Add variables for only the tables you would like to disable:
vars:
netsuite2__multibook_accounting_enabled: true # False by default. Disable `accountingbooksubsidiary` and `accountingbook` if you are not using the Multi-Book Accounting feature
netsuite2__using_exchange_rate: false #True by default. Disable `exchange_rate` if you don't utilize exchange rates. If you set this variable to false, ensure it is scoped globally so that the `netsuite_source` package can access it as well.
netsuite2__using_vendor_categories: false # True by default. Disable `vendorcategory` if you don't categorize your vendors
netsuite2__using_jobs: false # True by default. Disable `job` if you don't use jobs
netsuite2__using_employees: false # True by default. Disable `employee` if you don't use employees.Note: The Netsuite dbt package currently only supports disabling transforms of Multi-Book Accounting related tables (
accountingbooksubsidiaryandaccountingbook) and thevendorcategoryandjobsource tables. Please create an issue to request additional tables and/or features to exclude.To determine if a table or field is activated by a feature, access the Records Catalog.
To include accounting_book_id and accounting_book_name columns in the end models, set the below variable to true in your dbt_project.yml. This feature is disabled by default.
Notes:
- If you choose to enable this feature, this will add rows for transactions for any non-primary
accounting_book_id, and your downstream use cases may need to be adjusted.- The surrogate keys for the end models are dynamically generated depending on the enabled/disabled features, so adding these rows will not cause test failures.
- If you are leveraging a
*_pass_through_columnsvariable to include the added columns, you may need to remove them to avoid a duplicate column error.
vars:
netsuite2__multibook_accounting_enabled: true # False by default.IMPORTANT: If you are using multi-book accounting, this variable must be set to true, or you will see test failures in your data.
To include to_subsidiary_id and to_subsidiary_name columns in the end models, set the below variable to true in your dbt_project.yml. This feature is disabled by default. You will also need to be using exchange rates, which is enabled by default.
Notes:
- If you choose to enable this feature, this will add rows for transactions where
to_subsidiaryis not a top-level subsidiary. Your downstream use cases may need to be adjusted.- The surrogate keys for the end models are dynamically generated depending on the enabled/disabled features, so adding these rows will not cause test failures.
- If you are leveraging a
*_pass_through_columnsvariable to include the added columns, you may need to remove them to avoid a duplicate column error.
vars:
netsuite2__using_to_subsidiary: true # False by default.This package includes all source columns defined in the macros folder. To add additional columns to this package, do so by adding our pass-through column variables to your dbt_project.yml file:
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"Additionally, this package allows users to pass columns from the netsuite__transaction_details table into
the netsuite__balance_sheet and netsuite__income_statement tables. See below for an example
of how to passthrough transaction detail columns into the respective balance sheet and income statement final tables
within your dbt_project.yml file.
vars:
balance_sheet_transaction_detail_columns: ['company_name','vendor_name']
income_statement_transaction_detail_columns: ['is_account_intercompany','location_name']By default, this package builds the Netsuite staging models within a schema titled (<target_schema> + _netsuite_source) and your Netsuite modeling models within a schema titled (<target_schema> + _netsuite) in your destination. If this is not where you would like your Netsuite data to be written to, add the following configuration to your root dbt_project.yml file:
models:
netsuite_source:
+schema: my_new_schema_name # leave blank for just the target_schema
netsuite:
+schema: my_new_schema_name # leave blank for just the target_schemaIf 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 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.
Records from the source can sometimes arrive late. Since several of the models in this package are incremental, by default we look back 3 days from the _fivetran_synced_date of transaction records to ensure late arrivals are captured and avoiding the need for frequent full refreshes. While the frequency can be reduced, we still recommend running dbt --full-refresh periodically to maintain data quality of the models.
To change the default lookback window, add the following variable to your dbt_project.yml file:
vars:
netsuite:
lookback_window: number_of_days # default is 3Since pricing and runtime priorities vary by customer, by default we chose to materialize the below models as tables instead of an incremental materialization for Bigquery and Databricks. For more information on this decision, see the Incremental Strategy section of the DECISIONLOG.
If you wish to enable incremental materializations leveraging the merge strategy, you can add the below materialization settings to your dbt_project.yml file. You only need to add lines for the specific model materializations you wish to change.
models:
netsuite:
netsuite2:
netsuite2__income_statement:
+materialized: incremental # default is table for Bigquery and Databricks
netsuite2__transaction_details:
+materialized: incremental # default is table for Bigquery and Databricks
netsuite2__balance_sheet:
+materialized: incremental # default is table for Bigquery and Databricks
intermediate:
int_netsuite2__tran_with_converted_amounts:
+materialized: incremental # default is ephemeral for Bigquery and Databricks(Optional) Step 7: Produce Analytics-Ready Reports with Streamlit App (Bigquery and Snowflake users only)
For those who want to take their reports a step further, our team has created the Fivetran Netsuite Streamlit App to generate end model visualizations based off of the reports we created in this package. This way you can replicate much of the reporting you see internally in Netsuite and automate a lot of the work needed to report on your core metrics.
We recommend following the instructions here to fork the app for your own data and create end reports leveraging our Netsuite models. You can see a sample version of these reports here.
Expand for 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. 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/netsuite_source
version: [">=0.11.0", "<0.12.0"]
- 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 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 on the best workflow for contributing to a package.
- 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.