Stop manually creating tickets for failed pipelines and tracking data engineering work across disconnected systems. Redbird AI syncs Airflow pipeline runs with Jira issues automatically, so data ops incidents surface to engineering teams without context switching.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When an Airflow task fails, automatically create a Jira issue with the full error stack trace, affected DAG, task instance details, and execution logs. Engineering teams get all the context they need to debug without digging through Airflow logs.
Create Jira issues for planned Airflow DAG updates and automatically tag them with pipeline metadata. When the ticket moves to Done, Redbird can trigger a validation run or update pipeline documentation with completion details.
When an Airflow task fails but succeeds on retry, automatically resolve the corresponding Jira incident ticket and add a comment with retry attempt details. Keeps your backlog clean and reflects actual system state without manual updates.
Combine Jira sprint completion data with Airflow DAG deployment history to show which pipelines were built, updated, or deprecated each sprint. Surface engineering velocity metrics that include actual production pipeline outcomes, not just ticket status.
When Airflow detects an SLA breach on a critical pipeline, find related Jira issues for downstream services and add comments with impact details. Data platform issues automatically surface to teams waiting on that data.
After resolving pipeline incidents tracked in Jira, sync ticket discussions, resolution steps, and root cause notes back to Airflow as DAG metadata. Build a searchable history of pipeline issues within your orchestration platform for future debugging.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Airflow and Jira with OAuth or API credentials. Redbird never stores your data — it just passes through.
Tell Redbird what to do in plain language — no SQL, no code, no configuration files required.
Redbird shows you exactly what it will do before running anything. Approve the workflow, set a schedule, and switch it on.
Workflows run on your schedule or on triggers. Every run is logged. Adjust with natural language at any time.
Redbird understands Airflow's DAG structure, task dependencies, and execution metadata alongside Jira's issue hierarchies, sprint workflows, and custom fields — no manual mapping required.
Redbird's AI interprets your Airflow DAG definitions, task instance states, SLA configurations, and XCom data, then maps them intelligently to Jira issue types, custom fields, and project workflows. It understands when a task failure is an incident versus a known flaky test, and routes context accordingly. The system learns your team's conventions—whether you track data pipeline work in epics, link issues to specific database tables, or use custom fields for data lineage—and automates ticket creation and updates that match your existing practices.
faster incident response vs manually copying Airflow errors into tickets
Redbird can pull from Airflow and Jira simultaneously, merge the results, and format a polished report — sent on a schedule or on demand.
Set conditions in natural language. Get notified in Slack or email the moment a threshold is crossed in either Airflow or Jira.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from Airflow into Jira, or from Jira back into Airflow. Resolve conflicts with configurable merge rules.
Every workflow run is logged — what ran, what changed, and why. Replay or revert any individual step at any time.
Start automations from any Airflow pipeline event or Jira issue transition and take action across both platforms.
Fires when an Airflow DAG run completes with a failed status, including all task-level error details.
Triggers when a task attempts a retry after initial failure, capturing retry count and wait time.
Activates when a task or DAG misses its defined SLA threshold, with timing and duration context.
Manually start a specific DAG execution with custom configuration parameters from Jira workflows.
Add or modify metadata tags, notes, or custom attributes on Airflow tasks and DAG runs.
Reset failed task instances to enable re-runs without manual Airflow UI intervention.
Fires when a Jira issue transitions between statuses like To Do, In Progress, or Done.
Triggers when an issue's priority is raised to Critical or Blocker, indicating urgent data platform needs.
Activates at sprint close, providing access to all completed issues and velocity metrics for that iteration.
Generate new Jira tickets with specific issue types, projects, and custom field values from pipeline events.
Append detailed comments to existing issues with Airflow execution logs, error traces, or resolution notes.
Move issues through your workflow automatically based on pipeline success, retry, or failure states.
Connect Airflow and Jira in minutes. Stop manually syncing pipeline failures to tickets and start automating the loop between data ops and engineering teams.