TOBIKO CLOUD × AIRFLOW
Modernize and Scale Your Airflow Pipelines
Tobiko Cloud brings cloud-native intelligence to Apache Airflow. Keep the Airflow UI your team knows. Add the precision, scalability, and deployment control that modern enterprise data platforms demand.
Tobiko Cloud simplifies and supercharges your pipelines
One DAG = One (or few) schedules
Schedule sprawl, limited model-level flexibility
Model-level scheduling (per-model cron)
Cleaner orchestration, fewer DAGs, schedule-level SLAs
Global concurrency + dynamic task mapping
Still requires manual tuning, pool setup, and monitoring overhead
Intra-model concurrency (batch_size, batch_concurrency)
Faster backfills, partition-parallel ETL, improved throughput
Manual test → deploy process
Release risk, duplicate configurations, missed production updates
Code-defined promotion with lineage checks
Fewer errors, faster QA, safer deploys
Disconnected logs + run metadata
Slower triage, longer debugging cycles, and higher MTTR (Mean Time to Recovery), as engineers must manually trace issues across fragmented systems
DAG + run observability across systems
Streamlined, click-through debugging, fewer escalations, real-time pipeline observability
Architecture Snapshot
Swipe left/right to see the entire diagram.
Scheduler
- Native, automated per-model job scheduling
- No CI/CD configuration
- Unlimited concurrent running jobs
- Pause individual production or model runs
- Fully Managed State/Data Catalog
- Isolated Python environments
Alerts
- Runtime alerts and root-cause identification for failures
- Configurable alerts for custom measurements
- Context-aware troubleshooting guidance
- Notifications through pager duty, email, and Slack. Webhook and Datadog integrations coming soon
Debugger
- Comprehensive, centralized view on errors, including pull requests, query consoles, logs, and DMs
- Traceability for each plan or run, including up/downstream dependencies, code definition at time of failure, recent model modifications, and other contextual execution metadata
Warehouse Cost Tracking
- Understand which models are contributing most to warehouse cost
- View warehouse cost changes over time
Advanced Change Categorization
- Automatic detection of downstream processes requiring updates after column modifications or removals
- Advanced column-level lineage impact analysis to minimize backfills
Cross-Database Diffing
- Detect discrepancies between datasets across multiple databases to validate migrations
- Leverage hashing algorithm for data comparison without costly full joins
Tobiko Cloud Simplifies and Supercharges Your Pipelines
Accelerated Delivery
Reduce pipeline deployment cycles from weeks to hours
Governed Environments
Eliminate staging/production overlap and data mishaps
Optimized Compute & Cost
Concurrency handled across models and partitions by default
Platform Visibility
Surface root causes in one interface across orchestration + execution layers
Flexible Deployment
Run fully in the cloud or in hybrid mode to meet security, governance, and data locality needs
Tobiko Cloud fills the gaps that still exist in Airflow 3.0:
- Airflow still limits scheduling to DAG-level definitions (Tobiko = per-model)
- Airflow's task mapping doesn't support native model batching (Tobiko = batch concurrency)
- Airflow 3.0 offers more observability, but Tobiko brings unified debugging across cloud, DAG, and execution logs
Use the orchestrator you know. Upgrade it with the intelligence you need.
