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Observability built for Data Engineers

Databand helps data engineering teams catch data pipeline issues and trace the impact of problems on data deliveries. Databand’s platform includes an application for visualizing pipeline metadata, alert engine, and open source library for integrating with your Python, Java, Scala, or SQL data processes.

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Pipeline metadata monitoring

An open, flexible system to gather all the unified metadata you need, from your scheduler, pipeline code, and data warehouse.

SLAs

Monitor the timing of your tasks and pipelines from scheduled times, actual start times, completion times and more.

Internal Task Statuses

Get function and query-level visibility inside your pipelines, so you can drill into where errors are arising.

Application Logs

Access execution details, success logs, error messages, and runtime states from your data tasks, queries, and functions.

System Resources

See pipeline resource consumption levels and durations from underlying cloud or compute systems.

Data Lineage

Track data inputs and outputs, within and across your tasks and pipelines.

Data Quality Metrics

Monitor data schemas, data distributions, completeness, and custom metrics.

Connect in minutes

Easy to setup. Easy to use. Instant visibility.

  • Gain ops metrics to zero-in on the pipelines prone to failure or causing data delays
  • Access out-of-the-box data quality metrics with no code changes
  • Track usage KPIs to optimize your cloud data services
  • Automate alerting to Slack, Pagerduty, and custom alert targets

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Metrics from all your tools in one place

Unified visibility into all your data tools

Use cases

Data platform teams use Databand to cover a range of activities, from big data analytics to machine learning

Monitoring analytics pipelines

  • Guarantee on-time delivery of the data that powers your analytical dashboards and business intelligence report

Tracking cloud migrations

  • Ensure replication processes produce consistent and reliable data

Automated data scanning

  • Automate health checks on high-value data in your data lake and warehouses

Machine learning tracking and experimentation

  • Monitor model performance scores, catch data drift, and capture corruptions to improve your model accuracy and prevent degradations

Start a free trial or demo

Contact us for a free trial or to see a demo of the solution in action.