Feature/package updates

.github/ISSUE_TEMPLATE/config.yml

@@ -1,4 +1,7 @@
 contact_links:
+  - name: Provide feedback to our dbt package team
+    url: https://www.surveymonkey.com/r/DQ7K7WW
+    about: Fill out our survey form to provide valuable feedback to the Fivetran team developing and maintaining the dbt packages.
   - name: Ask a question during our office hours
     url: https://calendly.com/fivetran-solutions-team/fivetran-solutions-team-office-hours
     about: Schedule time during the external office hours block with the Fivetran Analytics Engineering team for support

CHANGELOG.md

@@ -1,3 +1,6 @@
+# dbt_lever v0.4.0
+🎉 Applying Package Standardization 🎉
+We are applying standardization updates to be more consistent across our documentation.
 # dbt_lever v0.3.0
 🎉 dbt v1.0.0 Compatibility 🎉
 ## 🚨 Breaking Changes 🚨

README.md

@@ -1,17 +1,31 @@
-[![Apache License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) 
-# Lever [(docs)](https://pensive-murdock-e1bb8a.netlify.app/#!/overview)
-
-This package models Lever data from [Fivetran's Opportunity endpoint connector](https://fivetran.com/docs/applications/lever). It uses data in the format described by [this ERD](https://fivetran.com/docs/applications/lever#schemainformation).
+<p align="center">
+    <a alt="License"
+        href="https://github.com/fivetran/dbt_lever/blob/main/LICENSE">
+        <img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" /></a>
+    <a alt="Fivetran-Release"
+        href="https://fivetran.com/docs/getting-started/core-concepts#releasephases">
+        <img src="https://img.shields.io/badge/Fivetran Release Phase-_Beta-orange.svg" /></a>
+    <a alt="dbt-core">
+        <img src="https://img.shields.io/badge/dbt_Core™_version->=1.0.0_,<2.0.0-orange.svg" /></a>
+    <a alt="Maintained?">
+        <img src="https://img.shields.io/badge/Maintained%3F-yes-green.svg" /></a>
+    <a alt="PRs">
+        <img src="https://img.shields.io/badge/Contributions-welcome-blueviolet" /></a>
+</p>
+
+# Lever Transformation dbt Package ([Docs](https://fivetran.github.io/dbt_lever/))
+# 📣 What does this dbt package do?
+- Produces modeled tables that leverage Lever data from [Fivetran's connector](https://fivetran.com/docs/applications/lever) in the format described by [this ERD](https://fivetran.com/docs/applications/lever#schemainformation) and builds off the output of our [Lever source package](https://github.com/fivetran/dbt_lever_source).
 > NOTE: If your Lever connector was created [prior to July 2020](https://fivetran.com/docs/applications/lever/changelog) or still uses the Candidate endpoint, you must fully re-sync your connector or set up a new connector to use Fivetran's Lever dbt packages.
 
 This package enables you to understand trends in recruiting, interviewing, and hiring at your company. It also provides recruiting stakeholders with information about individual opportunities, interviews, and jobs. It achieves this by:
 - Enriching the core opportunity, interview, job posting, and requisition tables with relevant pipeline data and metrics
 - Integrating the interview table with reviewer information and feedback
 - Calculating the velocity of opportunities through each pipeline stage, along with major job- and candidate-related attributes for segmented funnel analysis
 
-## Models
+The following table provides a detailed list of all models materialized within this package by default. 
+> TIP: See more details about these models in the package's [dbt docs site](https://fivetran.github.io/dbt_lever/#!/overview?g_v=1&g_e=seeds).
 
-This package contains transformation models, designed to work simultaneously with our [Lever source package](https://github.com/fivetran/dbt_lever_source). A dependency on the source package is declared in this package's `packages.yml` file, so it will automatically download when you run `dbt deps`. The primary outputs of this package are described below. Intermediate models are used to create these output models.
 
 | **model**                | **description**                                                                                                                                |
 | ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -21,32 +35,31 @@ This package contains transformation models, designed to work simultaneously wit
 | [lever__requisition_enhanced](https://github.com/fivetran/dbt_lever/blob/master/models/lever__requisition_enhanced.sql)             | Each record represents a unique job requisition, enriched with information about the requisition's hiring manager, owner, offers extended, and associated job postings. |
 | [lever__opportunity_stage_history](https://github.com/fivetran/dbt_lever/blob/master/models/lever__opportunity_stage_history.sql)             | Each record represents a stage that an opportunity has advanced to. Includes data about the time spent in each stage, the application source, the hiring manager, and the opportunity's owner, as well as the job's team, location, and department. |
 
-## Installation Instructions
-Check [dbt Hub](https://hub.getdbt.com/) for the latest installation instructions, or [read the dbt docs](https://docs.getdbt.com/docs/package-management) for more information on installing packages.
+# 🎯 How do I use the dbt package?
+
+## Step 1: Prerequisites
+To use this dbt package, you must have the following:
 
-Include in your `packages.yml`
+- At least one Fivetran Lever connector syncing data into your destination.
+- A **BigQuery**, **Snowflake**, or **Redshift** destination.
 
+## Step 2: Install the package
+Include the following lever_source package version in your `packages.yml` file:
+> TIP: Check [dbt Hub](https://hub.getdbt.com/) for the latest installation instructions or [read the dbt docs](https://docs.getdbt.com/docs/package-management) for more information on installing packages.
 ```yaml
 packages:
   - package: fivetran/lever
-    version: [">=0.3.0", "<0.4.0"]
+    version: [">=0.4.0", "<0.5.0"]
 ```
-
-## Configuration
-By default, this package looks for your Lever data in the `lever` schema of your [target database](https://docs.getdbt.com/docs/running-a-dbt-project/using-the-command-line-interface/configure-your-profile). If this is not where your Lever data is, add the following configuration to your `dbt_project.yml` file:
+## Step 3: Define database and schema variables
+By default, this package runs using your destination and the `lever` schema. If this is not where your Lever data is (for example, if your Lever schema is named `lever_fivetran`), add the following configuration to your root `dbt_project.yml` file:
 
 ```yml
-# dbt_project.yml
-
-...
-config-version: 2
-
 vars:
     lever_database: your_database_name
     lever_schema: your_schema_name 
 ```
-
-### Disabling Models
+## Step 4: Disable models for non-existent sources
 Your Lever connector might not sync every table that this package expects. If your syncs exclude certain tables, it is because you either don't use that functionality in Lever 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 for only the tables you want to disable:
 
 ```yml
@@ -58,6 +71,9 @@ vars:
     lever_using_requisitions: false # Disable if you do not have the requisition table, or if you do not want requisition related metrics reported
     lever_using_posting_tag: false # disable if you do not have (or want) the postings tag table
 ```
+## (Optional) Step 6: Additional configurations
+
+<details><summary>Expand for configurations</summary>
 
 ### Passing Through Custom Requisition Columns
 If you choose to include requisitions, the `REQUISITION` table may also have custom columns (all prefixed by `custom_field_`). To pass these columns through to the [enhanced requisition model](https://github.com/fivetran/dbt_lever/blob/master/models/lever__requisition_enhanced.sql), add the following variable to your `dbt_project.yml` file:
@@ -72,23 +88,61 @@ vars:
     lever_requisition_passthrough_columns: ['the', 'list', 'of', 'fields']
 ```
 
+### Change the build schema
+By default, this package builds the Lever staging models within a schema titled (`<target_schema>` + `_stg_lever`) and your Lever modeling models within a schema titled (`<target_schema>` + `_lever`) in your destination. If this is not where you would like your Lever data to be written to, add the following configuration to your root `dbt_project.yml` file:
+
+```yml
+models:
+    lever_source:
+      +schema: my_new_schema_name # leave blank for just the target_schema
+    lever:
+      +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`](https://github.com/fivetran/dbt_lever/blob/main/dbt_project.yml) variable declarations to see the expected names.
+
+```yml
+vars:
+    lever_<default_source_table_name>_identifier: your_table_name 
+```
+</details>
+
+## (Optional) Step 7: Orchestrate your models with Fivetran Transformations for dbt Core™
+<details><summary>Expand for details</summary>
+<br>
+
+Fivetran offers the ability for you to orchestrate your dbt project through [Fivetran Transformations for dbt Core™](https://fivetran.com/docs/transformations/dbt). Learn how to set up your project for orchestration through Fivetran in our [Transformations for dbt Core setup guides](https://fivetran.com/docs/transformations/dbt#setupguide).
+</details>
+
+# 🔍 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](https://hub.getdbt.com/) 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.
+
+```yml
+packages:
+    - package: fivetran/lever_source
+      version: [">=0.4.0", "<0.5.0"]
+
+    - package: fivetran/fivetran_utils
+      version: [">=0.3.0", "<0.4.0"]
+
+    - package: dbt-labs/dbt_utils
+      version: [">=0.8.0", "<0.9.0"]
+```
+# 🙌 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](https://hub.getdbt.com/fivetran/lever/latest/) of the package and refer to the [CHANGELOG](https://github.com/fivetran/dbt_lever/blob/main/CHANGELOG.md) and release notes for more information on changes across versions.
+
 ## Contributions
-Don't see a model or specific metric you would have liked to be included? Notice any bugs when installing 
-and running the package? If so, we highly encourage and welcome contributions to this package! 
-Please create issues or open PRs against `main`. Check out [this post](https://discourse.getdbt.com/t/contributing-to-a-dbt-package/657) on the best workflow for contributing to a package.
-
-## Database Support
-This package has been tested on BigQuery, Snowflake and Redshift.
-
-## Resources:
-- Provide [feedback](https://www.surveymonkey.com/r/DQ7K7WW) on our existing dbt packages or what you'd like to see next
-- Have questions, feedback, or need help? Book a time during our office hours [here](https://calendly.com/fivetran-solutions-team/fivetran-solutions-team-office-hours) or email us at solutions@fivetran.com
-- Find all of Fivetran's pre-built dbt packages in our [dbt hub](https://hub.getdbt.com/fivetran/)
-- Learn how to orchestrate dbt transformations with Fivetran [here](https://fivetran.com/docs/transformations/dbt)
-- Learn more about Fivetran overall [in our docs](https://fivetran.com/docs)
-- Check out [Fivetran's blog](https://fivetran.com/blog)
-- Learn more about dbt [in the dbt docs](https://docs.getdbt.com/docs/introduction)
-- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers
-- Join the [chat](http://slack.getdbt.com/) on Slack for live discussions and support
-- Find [dbt events](https://events.getdbt.com) near you
-- Check out [the dbt blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices
+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](https://discourse.getdbt.com/t/contributing-to-a-dbt-package/657) on the best workflow for contributing to a package!
+
+# 🏪 Are there any resources available?
+- If you have questions or want to reach out for help, please refer to the [GitHub Issue](https://github.com/fivetran/dbt_lever/issues/new/choose) 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](https://www.surveymonkey.com/r/DQ7K7WW).
+- Have questions or want to just say hi? Book a time during our office hours [on Calendly](https://calendly.com/fivetran-solutions-team/fivetran-solutions-team-office-hours) or email us at solutions@fivetran.com.
+

dbt_project.yml

@@ -1,5 +1,5 @@
 name: 'lever'
-version: '0.3.0'
+version: '0.4.0'
 config-version: 2
 require-dbt-version: [">=1.0.0", "<2.0.0"]
 vars:

docs/catalog.json

@@ -0,0 +1 @@
+{"metadata": {"dbt_schema_version": "https://schemas.getdbt.com/dbt/catalog/v1.json", "dbt_version": "1.0.0", "generated_at": "2022-05-17T23:51:27.272866Z", "invocation_id": "8527b549-cfb6-403f-9d19-45c332bf6695", "env": {}}, "nodes": {"model.develop.my_first_dbt_model": {"metadata": {"type": "table", "schema": "dbt_renee", "name": "my_first_dbt_model", "database": "dbt-package-testing", "comment": null, "owner": null}, "columns": {"id": {"type": "INT64", "index": 1, "name": "id", "comment": null}}, "stats": {"num_bytes": {"id": "num_bytes", "label": "Approximate Size", "value": 8.0, "include": true, "description": "Approximate size of table as reported by BigQuery"}, "num_rows": {"id": "num_rows", "label": "# Rows", "value": 2.0, "include": true, "description": "Approximate count of rows in this table"}, "has_stats": {"id": "has_stats", "label": "Has Stats?", "value": true, "include": false, "description": "Indicates whether there are statistics for this table"}}, "unique_id": "model.develop.my_first_dbt_model"}, "model.develop.my_second_dbt_model": {"metadata": {"type": "view", "schema": "dbt_renee", "name": "my_second_dbt_model", "database": "dbt-package-testing", "comment": null, "owner": null}, "columns": {"id": {"type": "INT64", "index": 1, "name": "id", "comment": null}}, "stats": {"has_stats": {"id": "has_stats", "label": "Has Stats?", "value": false, "include": false, "description": "Indicates whether there are statistics for this table"}}, "unique_id": "model.develop.my_second_dbt_model"}}, "sources": {}, "errors": null}