fivetran/dbt_greenhouse_source

Data source models for Fivetran's Greenhouse connector built using dbt.

Greenhouse Source dbt Package (Docs)

📣 What does this dbt package do?

• Materializes Greenhouse staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Greenhouse 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 Greenhouse data through the dbt docs site.
• These tables are designed to work simultaneously with our Greenhouse 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 Greenhouse connector syncing data into your destination.
• A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

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

Include the following greenhouse_source 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/greenhouse_source
    version: [">=0.6.0", "<0.7.0"] # we recommend using ranges to capture non-breaking changes automatically

Step 3: Define database and schema variables

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

vars:
    greenhouse_database: your_database_name
    greenhouse_schema: your_schema_name 

Step 4: Disable models for non-existent sources

Your Greenhouse connector might not sync every table that this package expects. If your syncs exclude certain tables, it is because you either do not use that functionality in Greenhouse or have actively excluded some tables from your syncs.

To disable the corresponding functionality in the package, you must set the relevant config variables to false. By default, all variables are set to true. Alter variables only for the tables you want to disable:

vars:
    greenhouse_using_prospects: false # Disable if you do not use prospects and/or do not have the PROPECT_POOL and PROSPECT_STAGE tables synced
    greenhouse_using_eeoc: false # Disable if you do not have EEOC data synced and/or do not want to integrate it into the package models
    greenhouse_using_app_history: false # Disable if you do not have APPLICATION_HISTORY synced and/or do not want to run the application_history transform model
    greenhouse_using_job_office: false # Disable if you do not have JOB_OFFICE and/or OFFICE synced, or do not want to include offices in the job_enhanced transform model
    greenhouse_using_job_department: false # Disable if you do not have JOB_DEPARTMENT and/or DEPARTMENT synced, or do not want to include offices in the job_enhanced transform model

(Optional) Step 5: Additional configurations

Expand to view configurations

Passthrough Custom Columns

The Greenhouse APPLICATION, JOB, and CANDIDATE tables may have custom columns, all prefixed with custom_field_. To pass these columns along to the staging and final transformation models, add the following variables to your dbt_project.yml file:

vars:
    greenhouse_application_custom_columns: ['the', 'list', 'of', 'columns'] # these columns will be in the final application_enhanced model
    greenhouse_candidate_custom_columns: ['the', 'list', 'of', 'columns'] # these columns will be in the final application_enhanced model
    greenhouse_job_custom_columns: ['the', 'list', 'of', 'columns'] # these columns will be in the final job_enhanced model

Changing the Build Schema

By default this package will build the Greenhouse Source staging models within a schema titled (<target_schema> + _greenhouse). If this is not where you would like your staging models to be written to, add the following configuration to your dbt_project.yml file:

models:
    greenhouse_source:
        +schema: my_new_staging_models_schema # 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:
    greenhouse_<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. 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.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"]

🙌 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, 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].

dbt_greenhouse_source's Issues

[Feature] Update README

Is there an existing feature request for this?

• I have searched the existing issues

Describe the Feature

The README needs to updated to the current format.

Describe alternatives you've considered

No response

Are you interested in contributing this feature?

• Yes.
• Yes, but I will need assistance and will schedule time during your office hours for guidance.
• No.

Anything else?

No response

Disable job_office model in greenhouse_source

Hello,
We are looking to exclude the job_office model from our greenhouse integration. We have no job_office table landed in our data warehouse from our fivetran greenhouse sync. Submitting this issue per the note on the dbt hub (below). Can we get set up to disable the job_office model?

Thanks,
Annie

https://hub.getdbt.com/fivetran/greenhouse_source/latest/

vars:
    greenhouse_using_prospects: false # Disable if you do not use prospects and/or do not have the PROPECT_POOL and PROSPECT_STAGE tables synced
    greenhouse_using_eeoc: false # Disable if you do not have EEOC data synced and/or do not want to integrate it into the package models
    greenhouse_using_app_history: false # Disable if you do not have APPLICATION_HISTORY synced and/or do not want to run the application_history transform model

^
Note: This package only integrates the above variables. If you'd like to disable other models, please create an issue specifying which ones.

BUG - disable freshness tests for disabled source tables

Are you a current Fivetran customer?

i work @ fivetran

Describe the bug

freshness tests are producing errors for folks who do not have various source models and are disabling package logic related to them.

need to incorporate changes we made to hubspot basically fivetran/dbt_hubspot_source#43

Expected behavior

if you aren't using a source table, a freshness test should not be run on it

Are you interested in contributing to this package?

• Yes, I can do this and open a PR for your review.
• Possibly, but I'm not quite sure how to do this. I'd be happy to do a live coding session with someone to get this fixed.
• No, I'd prefer if someone else fixed this. I don't have the time and/or don't know what the root cause of the problem is.

[Feature] Add postgres capability

Is there an existing feature request for this?

• I have searched the existing issues

Describe the Feature

Add postgres capability

Describe alternatives you've considered

No response

Are you interested in contributing this feature?

• Yes.
• Yes, but I will need assistance and will schedule time during your office hours for guidance.
• No.

Anything else?

No response

[Bug] Timestamps should be cast for Redshift

Is there an existing issue for this?

• I have searched the existing issues

Describe the issue

There are downstream models in the dbt_greenhouse package that apply date transformations. If the user of the package is leveraging Redshift and the fields are synced as timestamptz then it will undoubtedly fail. We will want to cast the timestamp fields with {{ dbt_utils.type_timestamp() }} to avoid this error.

Relevant error log or model output

17:07:31    function pg_catalog.date_diff("unknown", timestamp with time zone, timestamp with time zone) does not exist
17:07:31    HINT:  No function matches the given name and argument types. You may need to add explicit type casts.

Expected behavior

The downstream models do not fail if the field is initially synced as timestamptz in Redshift

dbt Project configurations

N/A

Package versions

packages:

• package: fivetran/greenhouse_source
  version: [">=0.4.0", "<0.5.0"]

What database are you using dbt with?

redshift

dbt Version

v1.1.0

Additional Context

• Initial dbt Slack thread where this issue was brought up
• PR #18 that addresses this issue.

Are you willing to open a PR to help address this issue?

• Yes.
• Yes, but I will need assistance and will schedule time during our office hours for guidance
• No.

FEATURE - add dbt docs folder

Are you a Fivetran customer?

Fivetran created feature request

Is your feature request related to a problem? Please describe.

Not related to a problem.

Describe the solution you'd like

Add a /docs folder to the package so we may host the dbt docs in github pages.

[Feature] Update docs and seeds

Is there an existing feature request for this?

• I have searched the existing issues

Describe the Feature

Seeds and docs contain references to customs columns in application and job tables. Remove from seeds and docs for a clean docs regen.

Also review docs for any columns missing descriptions.

Describe alternatives you've considered

No response

Are you interested in contributing this feature?

• Yes.
• Yes, but I will need assistance and will schedule time during your office hours for guidance.
• No.

Anything else?

No response

[Feature] Databricks Compatibility

Copied from fivetran/dbt_mixpanel #34.

Is there an existing feature request for this?

• I have searched the existing issues

Describe the Feature

For Databricks Compatibility, add the following:

1. Buildkite testing:
   • Update pre-command (example)
   • Update pipeline.yml (example)
   • Update sample.profiles.yml (example)
   • Add the below to integration_tests/dbt_project.yml if it's not there:

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

2. For source packages, update src yml so a database won't be passed to spark (example or use below):

sources: 
  - name: <name>
    database: "{% if target.type != 'spark' %}{{ var('<name>_database', target.database) }}{% endif %}"

3. Update any incremental models to update partition_by for databricks and add current strategies if not present:

config(
        materialized='incremental',
        unique_key='<original unique key>',
        partition_by={'field': '<original field>', 'data_type': '<original data type>'} if target.type not in ('spark','databricks') else ['<original field>'],
        incremental_strategy = 'merge' if target.type not in ('postgres', 'redshift') else 'delete+insert',
        file_format = 'delta' 
)

Describe alternatives you've considered

No response

Are you interested in contributing this feature?

• Yes.
• Yes, but I will need assistance and will schedule time during your office hours for guidance.
• No.

Anything else?

No response

[Feature] Update collect_freshness config in src_greenhouse.yml to account for dbt macro logic

Is there an existing feature request for this?

• I have searched the existing issues

Describe the Feature

Our fivetran_utils collect_freshness macro was designed to override dbt's equivalent macro to allow for the enabling/disabling of variables when running dbt source snapshot-freshness. Thus if a source table does not exist, dbt will not run (and error on) a freshness test on the table.

dbt has since updated its macro in recent versions to basically provide the same functionality. So warnings are being thrown to upgrade the package logic.

We will want to modify all versions of the meta: is_enabled config in our src_greenhouse.yml package (example here) to match the config: enabled dbt logic. Once those dependencies are either updated or removed, we can further explore updates to our own internal collect_freshness macro.

Describe alternatives you've considered

At the moment, we will be proceeding forward with updating dbt_fivetran_utils to match the logic of dbt's collect_freshness macro. That way customers will no longer experience that warning.

More details can be found in this ticket.

Are you interested in contributing this feature?

• Yes.
• Yes, but I will need assistance and will schedule time during your office hours for guidance.
• No.

Anything else?

Customers raising this issue can be found in this dbt slack thread https://getdbt.slack.com/archives/C03SAHKKG2Z/p1683127164993329