Jira dbt Package
This dbt package transforms data from Fivetran's Jira connector into analytics-ready tables.
Resources
- Number of materialized models¹: 48
- Connector documentation
- dbt package documentation
What does this dbt package do?
This package enables you to better understand the workload, performance, and velocity of your team's work using Jira issues. It creates enriched models with metrics focused on daily issue history, workflow analysis, and team performance.
Output schema
Final output tables are generated in the following target schema:
<your_database>.<connector/schema_name>_jira
Final output tables
By default, this package materializes the following final tables:
| Table | Description |
|---|---|
| jira__daily_issue_field_history | History table with one row for each day an issue remained open, with additional details about the issue sprint, status, and story points (if enabled). For an example of how this data can be used, see this analysis query, which demonstrates how you might build a daily issue status category analysis on top of this table. Example Analytics Questions:
|
| jira__timestamp_issue_field_history | Table tracking field changes at timestamp level with validity periods. Each record shows complete field state during a time period with valid_from/valid_until timestamps. For an example of how this data can be used, see this analysis query, which demonstrates building an issue transition cumulative flow analysis. Example Analytics Questions:
|
| jira__issue_status_transitions | Issue status transition tracking with workflow analysis. Provides chronological view of status changes with timing metrics, transition direction analysis, and lifecycle indicators. Example Analytics Questions:
|
| jira__issue_enhanced | One row per Jira issue with enriched details about assignee, reporter, sprint, project, and current status, plus metrics on assignments and re-openings. Example Analytics Questions:
|
| jira__project_enhanced | One row per project with team member details, issue counts, work velocity metrics, and project scope information. Example Analytics Questions:
|
| jira__user_enhanced | One row per user with metrics on open and completed issues, and individual work velocity. Example Analytics Questions:
|
| jira__sprint_enhanced | One row per sprint with metrics on issues created, resolved, and carried over, plus story point estimates. Example Analytics Questions:
|
| jira__daily_sprint_issue_history | Daily snapshot of each sprint showing all associated issues from sprint start to end, useful for tracking progress over time. Example Analytics Questions:
|
¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.
Prerequisites
To use this dbt package, you must have the following:
- At least one Fivetran Jira connection syncing data into your destination.
- A BigQuery, Snowflake, Redshift, Databricks, or PostgreSQL destination.
How do I use the dbt package?
You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:
- To add the package in the Fivetran dashboard, follow our Quickstart guide.
- To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.
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 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. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.