Jira dbt Package (Docs)

What does this dbt package do?

• Produces modeled tables that leverage Jira data from Fivetran's connector in the format described by this ERD.
• Enables you to better understand the workload, performance, and velocity of your team's work using Jira issues. It performs the following actions:
  • Creates a daily issue history table so you can quickly create agile reports, such as burndown charts, along any issue field.
  • Enriches the core issue table with relevant data regarding its workflow and current state.
  • Aggregates bandwidth and issue velocity metrics along projects, users and sprints.
• Generates a comprehensive data dictionary of your source and modeled Jira 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
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). Example Analytics Questions: • How many issues, by sprint, were Closed or Blocked each week in Q1, 2025? • How many days, on average, does it take an issue to go from 'Accepted' to either 'Closed' or 'Blocked'?
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: • How many issues are currently blocked and who owns them? • What's the average time to resolution for high-priority bugs by assignee?
jira__project_enhanced	One row per project with team member details, issue counts, work velocity metrics, and project scope information. Example Analytics Questions: • Which projects have the highest velocity in terms of issues closed per sprint? • What is the ratio of unassigned open tickets to assigned open tickets by project?
jira__user_enhanced	One row per user with metrics on open and completed issues, and individual work velocity. Example Analytics Questions: • Who are the top performers in terms of issues resolved per month? • Which team members have the highest workload based on open issue count and how long, on average, have those issues been open?
jira__sprint_enhanced	One row per sprint with metrics on issues created, resolved, and carried over, plus story point estimates. Example Analytics Questions: • Which sprints had the highest velocity and what made them successful? • How many story points were planned vs. completed across recent sprints? • What percentage of issues are typically carried over from sprint to sprint?
jira__daily_sprint_issue_history	Daily snapshot of each sprint showing all associated issues from sprint start to completion, useful for tracking progress over time. Example Analytics Questions: • How many issues were active in each sprint on any given day? • What's the daily count of open vs. completed issues per sprint? • Which sprints had issues added or removed mid-sprint?

Materialized Models

Each Quickstart transformation job run materializes 44 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.

How do I use the dbt package?

Step 1: 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.

Databricks Dispatch Configuration

If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) 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']

Database Incremental Strategies

Models in this package that are materialized incrementally are configured to work with the different strategies available to each supported warehouse.

For BigQuery and Databricks All Purpose Cluster runtime destinations, we have chosen insert_overwrite as the default strategy, which benefits from the partitioning capability.

For Databricks SQL Warehouse destinations, models are materialized as tables without support for incremental runs.

For Snowflake, Redshift, and Postgres databases, we have chosen delete+insert as the default strategy.

Regardless of strategy, we recommend that users periodically run a --full-refresh to ensure a high level of data quality.

Step 2: Install the package

Include the following jira 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/jira
    version: [">=1.0.0", "<1.1.0"]

All required sources and staging models are now bundled into this transformation package. Do not include fivetran/jira_source in your packages.yml since this package has been deprecated.

Step 3: Define database and schema variables

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

vars:
    jira_database: your_destination_name
    jira_schema: your_schema_name 

Step 4: Disable models for non-existent sources

Your Jira connection may not sync every table that this package expects. If you do not have the SPRINT, COMPONENT, or VERSION tables synced, add the respective variables to your root dbt_project.yml file. Additionally, if you want to remove comment aggregations from your jira__issue_enhanced model, add the jira_include_comments variable to your root dbt_project.yml:

vars:
    jira_using_sprints: false    # Enabled by default. Disable if you do not have the sprint table or do not want sprint-related metrics reported.
    jira_using_components: false # Enabled by default. Disable if you do not have the component table or do not want component-related metrics reported.
    jira_using_versions: false   # Enabled by default. Disable if you do not have the versions table or do not want versions-related metrics reported.
    jira_using_priorities: false # Enabled by default. Disable if you are not using priorities in Jira.
    jira_include_comments: false # Enabled by default. Disabling will remove the aggregation of comments via the `count_comments` and `conversations` columns in the `jira__issue_enhanced` table.

(Optional) Step 5: Additional configurations

Controlling conversation aggregations in jira__issue_enhanced

The dbt_jira package offers variables to enable or disable conversation aggregations in the jira__issue_enhanced table. These settings allow you to manage the amount of data processed and avoid potential performance or limit issues with large datasets.

• jira_include_conversations: Controls only the conversation column in the jira__issue_enhanced table.
  • Default: Disabled for Redshift due to string size constraints; enabled for other supported warehouses.
  • Setting this to false removes the conversation column but retains the count_comments field if jira_include_comments is still enabled. This is useful if you want a comment count without the full conversation details.

In your dbt_project.yml file:

vars:
  jira_include_conversations: false/true # Disabled by default for Redshift; enabled for other supported warehouses.

Define daily issue field history columns

The jira__daily_issue_field_history model generates historical data for the columns specified by the issue_field_history_columns variable. By default, the only columns tracked are status, status_id,sprint, story_points and story_point_estimate, but all fields found in the Jira FIELD table's field_name column can be included in this model. The most recent value of any tracked column is also captured in jira__issue_enhanced.

If you would like to change these columns, add the following configuration to your dbt_project.yml file. After adding the columns to your dbt_project.yml file, run the dbt run --full-refresh command to fully refresh any existing models:

IMPORTANT: If you wish to use a custom field, be sure to list the field_name and not the field_id. The corresponding field_name can be found in the stg_jira__field model.

vars:
    issue_field_history_columns: ['the', 'list', 'of', 'field', 'names']

Adjust the field-grain for issue field history transformations if duplicate field names

This package provides the option to use field_name instead of field_id as the field-grain for issue field history transformations. By default, the package strictly partitions and joins issue field data using field_id. However, this assumes that it is impossible to have fields with the same name in Jira. For instance, it is very easy to create another Sprint field, and different Jira users across your organization may choose the wrong or inconsistent version of the field. As such, the jira_field_grain variable may be adjusted to change the field-grain behavior of the issue field history models. You may adjust the variable using the following configuration in your root dbt_project.yml.

vars:
    jira_field_grain: 'field_name' # field_id by default

Extend the history of an issue past its closing date

This packages allows you the option to utilize a buffer variable to bring in issues past their date of close. This is because issues can be left unresolved past that date. This buffer variable ensures that this daily issue history will not cut off field updates to these particular issues.

You may adjust the variable using the following configuration in your root dbt_project.yml.

vars:
    jira_issue_history_buffer: insert_number_of_months # 1 by default

Change the build schema

By default, this package builds the Jira staging models within a schema titled (<target_schema> + _jira_source) and your Jira modeling models within a schema titled (<target_schema> + _jira) in your destination. If this is not where you would like your Jira data to be written to, add the following configuration to your root dbt_project.yml file:

models:
    jira:
      +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.
      staging:
        +schema: my_new_schema_name # Leave +schema: blank to use the default 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:
    jira_<default_source_table_name>_identifier: your_table_name 

Lookback Window

Records from the source may occasionally arrive late. To handle this, we implement a one-week lookback in our incremental models to capture late arrivals without requiring frequent full refreshes. The lookback is structured in weekly increments, as the incremental logic is based on weekly periods. While the frequency of full refreshes 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:
  jira:
    lookback_window: number_of_weeks # default is 1

(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™

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.

Does this package have dependencies?

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.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"]

    - package: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

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 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 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, 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.