This package enables you to better understand the workload, performance, and velocity of work done by your team using Jira issues. It achieves this by:
- Creating a daily issue history table to enable the quick creation of agile reports, such as burndown charts, along any issue field
- Enriching the core issue table with relevant data regarding its workflow and current state
- Aggregating bandwidth and issue velocity metrics along projects and users
Please be aware the dbt_jira and dbt_jira_source packages will only work with the Fivetran Jira schema released after September 10, 2020. If your Jira connector was set up prior to September 10, 2020, you will need to fully resync or set up a new Jira connector in order for the Fivetran dbt Jira packages to work.
This package contains transformation models, designed to work simultaneously with our Jira source package. 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.
|jira__daily_issue_field_history||Each record represents a day in which an issue remained open, complete with the issue’s sprint, its status, and the values of any fields specified by the
|jira__issue_enhanced||Each record represents a Jira issue, enriched with data about its current assignee, reporter, sprint, epic, project, resolution, issue type, priority, and status. Also includes metrics reflecting assignments, sprint rollovers, and re-openings of the issue. Note: all epics are considered
|jira__project_enhanced||Each record represents a project, enriched with data about the users involved, how many issues have been opened or closed, the velocity of work, and the breadth of the project (ie its components and epics).|
|jira__user_enhanced||Each record represents a user, enriched with metrics regarding their open issues, completed issues, the projects they work on, and the velocity of their work.|
packages: - package: fivetran/jira version: [">=0.8.0", "<0.9.0"]
By default, this package looks for your Jira data in the
jira schema of your target database. If this is not where your Jira data is, add the following configuration to your
... config-version: 2 vars: jira_database: your_database_name jira_schema: your_schema_name
Daily Issue Field History Columnslink
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
sprint, but all fields found in the
field_name column within the Jira
FIELD table can be included in this model. The most recent value of any tracked column is also captured in
If you would like to change these columns, add the following configuration to your dbt_project.yml file. Then, after adding the columns to your
dbt_project.yml file, run the
dbt run --full-refresh command to fully refresh any existing models.
... config-version: 2 vars: jira: issue_field_history_columns: ['the', 'list', 'of', 'field', 'names']
statuswill always be tracked, as they are necessary for creating common agile reports.
Extending an Issue’s History Periodlink
This package will create a row in
jira__daily_issue_field_history for each day that an issue is open or being updated. For currently open issues, the latest date will be the current date. For closed issues, the latest date will be when the issue was last resolved or updated in any way, plus a buffer period that is by default equal to 1 month. This buffer exists for two reasons:
- The daily issue field history model is materialized incrementally, and if your closed issues are being opened or updated often, this will avoid requiring a full refresh to catch these changes.
- You may want to create a longer timeline of issues, regardless of their status, for easier reporting.
If you would like to extend this buffer period to longer than 1 month, add the following configuration to your
... config-version: 2 vars: jira_issue_history_buffer: integer_number_of_months # default is an interval of 1 month
It’s possible that your Jira 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 functionality in Jira 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:
... config-version: 2 vars: jira_using_sprints: false # Disable if you do not have the sprint table, or if you do not want sprint related metrics reported jira_using_versions: false # Disable if you do not have the version table, or if you do not want version related metrics reported jira_using_components: false # Disable if you do not have the component table, or if you do not want component related metrics reported jira_using_priorities: false # disable if you are not using priorities in Jira jira_include_comments: false # this package aggregates issue comments so that you have a single view of all your comments in the jira__issue_enhanced table. This can cause limit errors if you have a large dataset. Disable to remove this functionality.
Changing the Build Schemalink
By default this package will build the Jira staging models within a schema titled (<target_schema> +
_stg_jira) and Jira final models within a schema titled (<target_schema> +
jira) in your target database. If this is not where you would like your modeled Jira data to be written to, add the following configuration to your
... models: jira: +schema: my_new_schema_name # leave blank for just the target_schema jira_source: +schema: my_new_schema_name # leave blank for just the target_schema
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 on the best workflow for contributing to a package.
This package has been tested on BigQuery, Snowflake, Redshift, and Postgres.
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