Discover where data pipelines are broken before bad data gets through
Instantly trace the root cause of pipeline failures and data quality issues. Observability purpose-built for Data Engineers.
Uncover new metrics to resolve data issues fast
From your data pipelines
- Zoom in to your most common task errors and why jobs are failing
- Identify drifts in data quality metrics
- Catch increases in task durations and likely SLA misses
- Measure spikes in query costs and resource consumption
From your data lake
- See how data tables and files are updating and changing over time
- Identify resource bottlenecks and inefficient database configurations
- Track how pipelines are reading and writing data
- Alert on changes in sizes, schemas, distributions, and custom metrics
A solution for unified pipeline monitoring
Databand collects metadata from your various pipeline services and helps you focus your attention on the most critical SLA issues, task failures, and data quality problems.
Quickly catch when pipelines deviate from normal baselines
Track all information in one place – data quality, data lineage, system resource information, and job durations
Integrate metadata from all data infrastructure levels, from orchestrator to data lake
Native to your data stack
Quickly integrate with best of breed data pipelining tools.
Capture schedule and run information from your schedulers, CRON systems and orchestrators.
Consolidate logs and error messages from your data ingestion, ETL, and ML code.
Understand performance and resource consumption levels from databases and data compute engines.
Track data quality metrics and data lineage across pipeline input and output, files, and data tables.
Start a free trial or demo
Contact us for a free trial or to see a demo of the solution in action.