update_model_and_add_changelog_entry

Author: fivetran-savage

CHANGELOG.md

@@ -24,7 +24,8 @@
 - Added team-related test data to `issue.csv`, `issue_field_history.csv`, and `field.csv` seed files.
 - Created `get_team_columns` macro for consistent team column definitions.
 - Updated package variables to include team source reference and team identifier configuration.
-- Created consistency tests for new end models. 
+- Created consistency tests for new end models.
+- Created new analysis folder with `jira__issue_cumulative_flow_analysis` model. See analysis [README.md](https://github.com/fivetran/dbt_jira/blob/main/analysis/README.md) for more details on how to use this model for your Jira reporting.
 
 # dbt_jira v1.0.0
 

analysis/README.md

@@ -1,12 +1,12 @@
 # Jira Analysis
-> Note: The compiled sql within the analysis folder references the final model [jira__issue_status_transitions](https://github.com/fivetran/dbt_jira/blob/master/models/jira__issue_status_transitions.sql). As such, prior to
+> Note: The compiled sql within the analysis folder references the final models [jira__issue_status_transitions](https://github.com/fivetran/dbt_jira/blob/master/models/jira__issue_status_transitions.sql) and [jira__timestamp_issue_field_history](https://github.com/fivetran/dbt_jira/blob/master/models/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__daily_issue_status_category_analysis](https://github.com/fivetran/dbt_jira/blob/master/analysis/jira__daily_issue_status_category_analysis.sql) | 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 in each 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. |
+| [jira__issue_cumulative_flow_analysis](https://github.com/fivetran/dbt_jira/blob/master/analysis/jira__issue_cumulative_flow_analysis.sql) | 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. |
 
 
 
@@ -17,7 +17,7 @@ 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__daily_issue_status_category_analysis` code and run in your data warehouse.
+- 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.
 
@@ -29,7 +29,7 @@ Please create issues or open PRs against `master`. Check out [this post](https:/
 
 
 ## Database Support
-This package has been tested on BigQuery, Snowflake and Redshift.
+This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.
 
 
 ## Are there any resources available?

…daily_issue_status_category_analysis.sql …jira__issue_cumulative_flow_analysis.sqlanalysis/jira__daily_issue_status_category_analysis.sql renamed to analysis/jira__issue_cumulative_flow_analysis.sql analysis/jira__daily_issue_status_category_analysis.sql renamed to analysis/jira__issue_cumulative_flow_analysis.sql

@@ -1,7 +1,3 @@
--- grab non hard-coded issue_field_history columns
-{% set issue_field_history_columns = var('issue_field_history_columns', []) %}
-
-
 with field_history as (
  select *
  from {{ ref('jira__timestamp_issue_field_history') }}
@@ -12,7 +8,7 @@ status_transitions as (
  from {{ ref('jira__issue_status_transitions') }}
 ),
 
----if you prefer to keep anayltics at the status level,substitute 'status' for 'status_category' throughout this model
+---if you prefer to keep analytics at the status level, substitute 'status' for 'status_category' throughout this model
 joined as (
     select 
     transitions.transition_at,
@@ -32,28 +28,28 @@ joined as (
         and transitions.transition_at = field_history.valid_from
 ),
 
----roll up to daily for reporing
+---roll up to daily for reporting
 daily_rollup as (
  select
     cast(transition_at as date) as status_category_date,
     project,
     team,
     status_category_name as status_category,
-    count(distinct issue_id) as issues_in_status_category,
+    count(distinct issue_id) as issues_in_new_status_category,
     round(avg(days_in_status),2) as avg_days_in_status_category,
     sum(started_work) as issues_started_work,
     sum(completed_work) as issues_completed_work,
     sum(reopened_work) as issues_reopened_work   
  from joined
-    group by 1,2,3,4
+  group by 1,2,3,4
 )
---add cumuliative flow calculation
+--add cumulative flow calculation
 select
   status_category_date,
   project,
   team,
   status_category,
-  issues_in_status_category,
+  issues_in_new_status_category,
   sum(issues_in_status_category) over (
     partition by project, team, status_category
     order by status_category_date

integration_tests/dbt_project.yml

@@ -5,10 +5,9 @@ profile: 'integration_tests'
 
 
 vars:
-  jira_using_components: false
 
  # Comment out the below when generating docs
-  issue_field_history_columns: ['summary', 'components','team' ,'project']  # @docs-ignore
+  issue_field_history_columns: ['summary', 'components', 'team' ,'project']  # @docs-ignore
 
   jira:
     jira_schema: jira_integrations_tests_42