Sync Databricks job failures, pipeline errors, and data quality issues directly into Jira tickets. Stop manually creating issues when pipelines break, tracking ML deployment status in spreadsheets, or chasing data engineers for updates on blocked workflows.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically generate Jira issues when ETL pipelines, notebook runs, or scheduled jobs fail in Databricks. Include cluster logs, error traces, and affected datasets in ticket descriptions. Assign to the correct team based on workspace or job tags.
When Delta Live Tables expectations fail or data quality checks detect anomalies, create prioritized Jira tickets with schema drift details and affected downstream dependencies. Link to notebooks and lineage graphs for faster debugging.
Update Jira story status when ML models move through stages in Databricks Model Registry — from staging to production. Add performance metrics, feature importance changes, and inference latency benchmarks as ticket comments.
Automatically rerun failed Databricks workflows when upstream data tickets are marked resolved in Jira. Match job tags to ticket labels and kick off backfill jobs with corrected source data references.
Capture Databricks cluster specs, runtime versions, and library dependencies when deploying new environments. Attach configuration JSON to Jira infrastructure tickets for audit trails and rollback documentation.
Pull current job run duration, compute costs, and data freshness metrics from Databricks into Jira tickets. Update engineering stories with actual resource usage versus estimated capacity for better sprint planning.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Databricks 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 Databricks workspace hierarchies, job orchestration patterns, and cluster configurations alongside Jira project structures, issue workflows, and engineering team assignments.
Redbird maps Databricks job metadata, notebook paths, Delta table schemas, and MLflow experiments to Jira issue types, custom fields, and sprint boards. It understands which pipeline failures need immediate P0 tickets versus batch job warnings that become backlog items. The AI routes cluster resource alerts to infrastructure teams and model drift issues to ML engineering backlogs based on workspace tags and team ownership patterns.
faster incident response when pipelines break
Redbird can pull from Databricks 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 Databricks 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 Databricks into Jira, or from Jira back into Databricks. 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 Databricks job event or Jira issue update — Redbird handles the connection logic between data platform and dev workflow.
Trigger when any scheduled job, notebook, or DLT pipeline fails, errors, or exceeds timeout thresholds in your workspace.
Trigger when MLflow models move between None, Staging, Production, or Archived in Model Registry.
Trigger when data quality expectations drop records or pipelines fail validation rules in DLT workflows.
Trigger existing jobs, notebooks, or multi-task workflows with specific parameters or override configurations.
Write structured data back to Delta tables with schema enforcement and merge logic for upserts.
Stop running clusters to control costs or restart with updated configurations based on external conditions.
Trigger when new tickets are created or existing issues change status, priority, assignee, or custom field values.
Trigger at the beginning or end of sprint cycles to sync planning data or generate delivery reports.
Trigger when tickets move to specific workflow states like Resolved, In Review, or Deployed in your board.
Generate new Jira tickets with project, issue type, description, priority, labels, and assignee from pipeline data.
Modify existing ticket fields, change status, update story points, or append comments with metric updates.
Create issue relationships like blocks, relates to, or caused by to track data dependencies across tickets.
Stop losing context between data pipelines and engineering tickets. Connect Databricks and Jira to automate incident tracking, sync deployment status, and keep your team aligned on data platform health.