Linear is a modern project management and issue tracking tool designed for software development teams. It provides a streamlined interface for managing issues, projects, cycles, and team workflows with powerful features for planning, tracking, and collaboration.

Configuring Linear as a Source

In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the Linear option from the list of connectors. Click Next and you’ll be prompted to add your access.

1. Add account access

You’ll need the following credentials from your Linear account:

Access Token: The token is generated after you authenticate with your Linear account. You should log in with an account that has sufficient permissions to access data from your Linear workspace.
Start Date: The earliest record date to sync.

Once you fill the configuration, click Next.

2. Select streams

Choose which data streams you want to sync - you can select all streams or pick specific ones that matter most to you.

Tip: The stream can be found more easily by typing its name.

Select the streams and click Next.

3. Configure data streams

Customize how you want your data to appear in your catalog. Select a name for each table (which will contain the fetched data) and the type of sync.

Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix and make this process faster!
Sync Type: you can choose between INCREMENTAL and FULL_TABLE.
- Incremental: every time the extraction happens, we’ll get only the new data - which is good if, for example, you want to keep every record ever fetched.
- Full table: every time the extraction happens, we’ll get the current state of the data - which is good if, for example, you don’t want to have deleted data in your catalog.

Once you are done configuring, click Next.

4. Configure data source

Describe your data source for easy identification within your organization, not exceeding 140 characters. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times). Optionally, you can determine when to execute a full sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while. Once you are ready, click Next to finalize the setup.

5. Check your new source

You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.

For you to be able to see it on your Catalog, you need at least one successful source run.

Streams and Fields

Below you’ll find all available data streams from Linear and their corresponding fields:

Issues

Users

Teams

Projects

Cycles

Issue Labels

Customers

Data Model

The following diagram illustrates the relationships between the core data streams in Linear. The arrows indicate the join keys that link the different entities, providing a clear overview of the data structure.

Use Cases for Data Analysis

Here are some valuable business intelligence use cases when consolidating Linear data, along with ready-to-use SQL queries that you can run on Explorer.

1. Team Velocity and Sprint Performance

Analyze team productivity and sprint completion rates across cycles. Business Value:

Track team velocity over time
Identify capacity planning opportunities
Measure sprint completion rates
Optimize cycle duration and scope

SQL code

WITH
  cycle_metrics AS (
    SELECT
      c.id AS cycle_id,
      c.name AS cycle_name,
      c.number AS cycle_number,
      c.team.name AS team_name,
      c.startsAt AS cycle_start,
      c.endsAt AS cycle_end,
      c.progress AS cycle_progress,
      DATE_DIFF('day', c.startsAt, c.endsAt) AS cycle_duration_days,
      CASE 
        WHEN c.completedAt IS NOT NULL THEN 'Completed'
        WHEN c.endsAt < CURRENT_TIMESTAMP THEN 'Overdue'
        WHEN c.startsAt <= CURRENT_TIMESTAMP THEN 'Active'
        ELSE 'Planned'
      END AS cycle_status
    FROM nekt_raw.linear_cycles c
    WHERE c.startsAt >= CURRENT_DATE - INTERVAL '6' MONTH
  ),
  issue_metrics AS (
    SELECT
      i.cycle.id AS cycle_id,
      COUNT(*) AS total_issues,
      COUNT(CASE WHEN i.completedAt IS NOT NULL THEN 1 END) AS completed_issues,
      SUM(COALESCE(i.estimate, 0)) AS total_story_points,
      SUM(CASE WHEN i.completedAt IS NOT NULL THEN COALESCE(i.estimate, 0) ELSE 0 END) AS completed_story_points,
      AVG(DATE_DIFF('hour', i.createdAt, COALESCE(i.completedAt, CURRENT_TIMESTAMP))) AS avg_completion_time_hours
    FROM nekt_raw.linear_issues i
    WHERE i.cycle.id IS NOT NULL
    GROUP BY i.cycle.id
  )
SELECT
  cm.team_name,
  cm.cycle_name,
  cm.cycle_number,
  cm.cycle_start,
  cm.cycle_end,
  cm.cycle_duration_days,
  cm.cycle_status,
  ROUND(cm.cycle_progress * 100, 2) AS progress_percentage,
  COALESCE(im.total_issues, 0) AS total_issues,
  COALESCE(im.completed_issues, 0) AS completed_issues,
  ROUND(
    CAST(COALESCE(im.completed_issues, 0) AS DOUBLE) / NULLIF(im.total_issues, 0) * 100,
    2
  ) AS completion_rate_percentage,
  COALESCE(im.total_story_points, 0) AS total_story_points,
  COALESCE(im.completed_story_points, 0) AS completed_story_points,
  ROUND(im.avg_completion_time_hours / 24.0, 2) AS avg_completion_time_days
FROM cycle_metrics cm
LEFT JOIN issue_metrics im ON cm.cycle_id = im.cycle_id
ORDER BY cm.team_name, cm.cycle_start DESC

2. Issue Resolution Analysis

Track issue resolution patterns and identify bottlenecks in the development process. Business Value:

Identify issues that take longest to resolve
Analyze resolution patterns by priority and team
Track issue aging and identify blockers
Optimize workflow efficiency

SQL code

WITH
  issue_analysis AS (
    SELECT
      i.id,
      i.identifier,
      i.title,
      i.priority,
      i.priorityLabel,
      i.team.name AS team_name,
      i.assignee.displayName AS assignee_name,
      i.state.name AS current_state,
      i.state.type AS state_type,
      i.createdAt,
      i.startedAt,
      i.completedAt,
      i.estimate,
      DATE_DIFF('day', i.createdAt, COALESCE(i.completedAt, CURRENT_TIMESTAMP)) AS age_days,
      CASE
        WHEN i.completedAt IS NOT NULL THEN 
          DATE_DIFF('day', i.createdAt, i.completedAt)
      END AS resolution_time_days,
      CASE
        WHEN i.completedAt IS NOT NULL THEN 'Completed'
        WHEN i.startedAt IS NOT NULL THEN 'In Progress'
        ELSE 'Not Started'
      END AS issue_status,
      CASE
        WHEN i.priority = 1 THEN 'Urgent'
        WHEN i.priority = 2 THEN 'High'
        WHEN i.priority = 3 THEN 'Medium'
        WHEN i.priority = 4 THEN 'Low'
        ELSE 'No Priority'
      END AS priority_category
    FROM nekt_raw.linear_issues i
    WHERE i.createdAt >= CURRENT_DATE - INTERVAL '3' MONTH
  ),
  team_summary AS (
    SELECT
      team_name,
      priority_category,
      COUNT(*) AS total_issues,
      COUNT(CASE WHEN issue_status = 'Completed' THEN 1 END) AS completed_issues,
      AVG(resolution_time_days) AS avg_resolution_days,
      PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY resolution_time_days) AS median_resolution_days,
      COUNT(CASE WHEN age_days > 30 AND issue_status != 'Completed' THEN 1 END) AS stale_issues
    FROM issue_analysis
    GROUP BY team_name, priority_category
  )
SELECT
  team_name,
  priority_category,
  total_issues,
  completed_issues,
  ROUND(
    CAST(completed_issues AS DOUBLE) / NULLIF(total_issues, 0) * 100,
    2
  ) AS completion_rate_percentage,
  ROUND(avg_resolution_days, 2) AS avg_resolution_days,
  ROUND(median_resolution_days, 2) AS median_resolution_days,
  stale_issues,
  ROUND(
    CAST(stale_issues AS DOUBLE) / NULLIF(total_issues, 0) * 100,
    2
  ) AS stale_issue_percentage
FROM team_summary
WHERE total_issues > 0
ORDER BY team_name, 
  CASE priority_category 
    WHEN 'Urgent' THEN 1 
    WHEN 'High' THEN 2 
    WHEN 'Medium' THEN 3 
    WHEN 'Low' THEN 4 
    ELSE 5 
  END

3. Project Progress and Resource Allocation

Monitor project health and team allocation across active projects. Business Value:

Track project progress and identify at-risk projects
Analyze resource allocation across projects
Monitor project timeline adherence
Optimize team capacity planning

SQL code

WITH
  project_metrics AS (
    SELECT
      p.id AS project_id,
      p.name AS project_name,
      p.state AS project_state,
      p.priority AS project_priority,
      p.progress AS project_progress,
      p.createdAt AS project_created,
      p.startedAt AS project_started,
      p.completedAt AS project_completed,
      p.targetDate AS target_date,
      p.lead.displayName AS project_lead,
      CASE
        WHEN p.completedAt IS NOT NULL THEN 'Completed'
        WHEN p.targetDate < CURRENT_DATE AND p.completedAt IS NULL THEN 'Overdue'
        WHEN p.startedAt IS NOT NULL THEN 'In Progress'
        ELSE 'Not Started'
      END AS project_status,
      CASE
        WHEN p.targetDate IS NOT NULL THEN
          DATE_DIFF('day', CURRENT_DATE, p.targetDate)
      END AS days_to_target
    FROM nekt_raw.linear_projects p
    WHERE p.archivedAt IS NULL
  ),
  project_issues AS (
    SELECT
      i.project.id AS project_id,
      COUNT(*) AS total_issues,
      COUNT(CASE WHEN i.completedAt IS NOT NULL THEN 1 END) AS completed_issues,
      COUNT(CASE WHEN i.startedAt IS NOT NULL AND i.completedAt IS NULL THEN 1 END) AS in_progress_issues,
      COUNT(CASE WHEN i.startedAt IS NULL THEN 1 END) AS not_started_issues,
      SUM(COALESCE(i.estimate, 0)) AS total_story_points,
      SUM(CASE WHEN i.completedAt IS NOT NULL THEN COALESCE(i.estimate, 0) ELSE 0 END) AS completed_story_points,
      COUNT(DISTINCT i.assignee.id) AS unique_assignees,
      COUNT(DISTINCT i.team.id) AS teams_involved
    FROM nekt_raw.linear_issues i
    WHERE i.project.id IS NOT NULL
    GROUP BY i.project.id
  )
SELECT
  pm.project_name,
  pm.project_state,
  pm.project_status,
  pm.project_lead,
  pm.project_created,
  pm.project_started,
  pm.target_date,
  pm.days_to_target,
  ROUND(pm.project_progress * 100, 2) AS progress_percentage,
  COALESCE(pi.total_issues, 0) AS total_issues,
  COALESCE(pi.completed_issues, 0) AS completed_issues,
  COALESCE(pi.in_progress_issues, 0) AS in_progress_issues,
  COALESCE(pi.not_started_issues, 0) AS not_started_issues,
  ROUND(
    CAST(COALESCE(pi.completed_issues, 0) AS DOUBLE) / NULLIF(pi.total_issues, 0) * 100,
    2
  ) AS issue_completion_rate,
  COALESCE(pi.total_story_points, 0) AS total_story_points,
  COALESCE(pi.completed_story_points, 0) AS completed_story_points,
  ROUND(
    CAST(COALESCE(pi.completed_story_points, 0) AS DOUBLE) / NULLIF(pi.total_story_points, 0) * 100,
    2
  ) AS story_points_completion_rate,
  COALESCE(pi.unique_assignees, 0) AS team_members_involved,
  COALESCE(pi.teams_involved, 0) AS teams_involved,
  CASE
    WHEN pm.project_status = 'Overdue' THEN 'At Risk'
    WHEN pm.days_to_target <= 7 AND pm.project_progress < 0.8 THEN 'At Risk'
    WHEN pm.project_progress >= 0.8 THEN 'On Track'
    ELSE 'Monitor'
  END AS health_status
FROM project_metrics pm
LEFT JOIN project_issues pi ON pm.project_id = pi.project_id
ORDER BY 
  CASE pm.project_status 
    WHEN 'Overdue' THEN 1 
    WHEN 'In Progress' THEN 2 
    ELSE 3 
  END,
  pm.days_to_target ASC NULLS LAST

Implementation Notes

Data Quality Considerations

Monitor issue state transitions for workflow analysis
Validate estimate accuracy against actual completion times
Track label usage consistency across teams
Ensure proper project and cycle assignment

Automation Opportunities

Schedule weekly team velocity reports
Set up alerts for overdue issues and projects
Automate sprint retrospective data collection
Generate monthly team performance dashboards

Best Practices

Use incremental sync for large datasets to improve performance
Set appropriate start_date to avoid syncing unnecessary historical data

These SQL transformations provide a foundation for comprehensive Linear analytics. Customize the date ranges, filters, and metrics based on your specific team workflows and reporting needs.

Get started

Using Nekt

Understand

Use cases

FAQ

Linear as a data source

Configuring Linear as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

1. Team Velocity and Sprint Performance

2. Issue Resolution Analysis

3. Project Progress and Resource Allocation

Implementation Notes

Data Quality Considerations

Automation Opportunities

Best Practices

Get started

Using Nekt

Understand

Use cases

FAQ

​Configuring Linear as a Source

​1. Add account access

​2. Select streams

​3. Configure data streams

​4. Configure data source

​5. Check your new source

​Streams and Fields

​Data Model

​Use Cases for Data Analysis

​1. Team Velocity and Sprint Performance

​2. Issue Resolution Analysis

​3. Project Progress and Resource Allocation

​Implementation Notes

​Data Quality Considerations

​Automation Opportunities

​Best Practices

Configuring Linear as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

1. Team Velocity and Sprint Performance

2. Issue Resolution Analysis

3. Project Progress and Resource Allocation

Implementation Notes

Data Quality Considerations

Automation Opportunities

Best Practices