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Jira Analysis

Note: The compiled sql within the analysis folder references the final models jira__issue_status_transitions and jira__timestamp_issue_field_history. As such, prior to compiling the provided sql to analyze issue status category metrics, you must first execute dbt run.

Analysis SQL

sql description
jira__issue_cumulative_flow_analysis The output of the compiled sql will generate daily metrics for issue status categories by joining status transitions with field history data. The analysis aggregates data by date, project, team, and status category to provide: count of distinct issues transitioning into a new status category, average days spent in status category, and counts of issues that started work, completed work, or reopened work. The SQL references the jira__issue_status_transitions and jira__timestamp_issue_field_history models. Aggregation granularity can be adjusted by adding/removing field names in the issue_field_history_columns var in your dbt project.yml. status can be used in place of status_category if desired by modifying the model accordingly.

SQL Compile Instructions

Leveraging the above sql is made possible by the analysis functionality of dbt. In order to compile the sql, you will perform the following steps:

  • Execute dbt run to create the package models.
  • Execute dbt compile to generate the target specific sql.
  • Navigate to your project's /target/compiled/jira/analysis directory.
  • Copy the jira__issue_cumulative_flow_analysis code and run in your data warehouse.
  • Confirm the issue status category metrics match your expected workflow patterns.
  • Analyze the daily counts, cumulative flow, and completion metrics to identify trends and bottlenecks in your development process.

Contributions

Don't see a compiled sql statement you would have liked to be included? Notice any bugs when compiling and running the analysis sql? If so, we highly encourage and welcome contributions to this package! Please create issues or open PRs against master. Check out this post on the best workflow for contributing to a package.

Database Support

This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.

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.