Monitor your data quality and pipeline performance

Observability for the cloud-native data stack

Gain visibility over pipelines running on tools like Airflow, Spark, Snowflake, BigQuery, and Kubernetes

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Challenges Today


Incomplete Information

Gaps in seeing the full content of data and health of jobs

Siloed Data

Pipeline logs, errors, and data quality metrics captured in isolated systems, impossible to correlate

Unpredictability

Inconsistent results and delays in data delivery

Our Solution

End to End Monitoring

Ensure quality data arrives in the right place, at the right time.

Specialized Alerting

Gain automated alerts to zero-in on pipeline issues and changes.

Faster Debugging

Expedite root cause analysis on pipeline failures.


How it Works


Connect Your Pipeline

Using Databand’s open source library and API, connect your pipeline orchestrator and jobs.

Track Metadata

Databand’s web service automatically tracks pipelines and all job metadata, enabling you to drill into logs, compare trends, and check data quality metrics.

Track Metadata

Databand’s web service automatically tracks pipelines and all job metadata, enabling you to drill into logs, compare trends, and check data quality metrics.

Generate Alerts

Access alerting functionality that’s customized for data processes, so that you can stay ahead of failures and separate signal from noise.

Built for the data engineering stack

Databand is specially built for pipelines running on tools like Spark, Airflow, and Snowflake.

Databand integrates seamlessly with the best of breed tools that run your data flows, collecting critical metadata so you have the info you need.

“Databand gives me the peace of mind that my DAGs will complete when they should, with the right data. And when things go wrong, I can immediately jump into where the problems are.”

- Head of Data, OptimalQ