Data observability platform for data engineers.

Databand’s proactive data observability platform helps you monitor and control your data’s quality, even when you can’t control your sources.

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Detect & resolve data incidents right at their source.

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Cross-Stack Visibility

View run details, error logs,  critical alerts,  and runtime states from all your data tasks.

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Identify Root Causes

When issues occur, drill into the source of errors or data corruption across your pipeline.

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Guarantee Data SLAs

Identify when anomalous durations or failures cause late data deliveries and missed SLAs.


Single pane of glass to resolve your data incidents.

Gain insights into data reliability and quality under one roof. Databand’s data observability platform provides a central place to define and receive alerts around data incidents. This way you can rapidly detect, alert, and resolve incidents without jumping from screen to screen.

  • Catalog and profile all alerts under one roof across your entire data stack.
  • Create custom alerts on incidents like missed data deliveries, unexpected schema changes, anomalies in column-level statistics, and more.
  • Route alerts to all your data stakeholders in real-time with Slack, PagerDuty, and email integrations.
Data Observability - Incident Management
Data Observability - Data Lineage


Pinpoint up & downstream data impacts.

Databand helps you see how data incidents impact your upstream and downstream data processes with end-to-end data lineage.

  • Save debugging time by automatically knowing what processes were impacted by the incident.
  • Clearly understand what pipelines and datasets are at risk of consuming corrupted data.
  • Prioritize data incidents better by knowing which alert is causing the most corruption.


Get the 5 steps to achieve proactive data observability.


Catch data process and pipeline errors earlier.

Connect your data processes and pipelines to detect missing operations, failed jobs, and run durations earlier with Databand’s data observability platform. 

  • Unify error logging to discover why a pipeline error occurred. 
  • Automatically track when missed or failed operations occur and show historical trends of impacted datasets.
  • Integrate with popular data process and pipeline tools like Apache Airflow, Spark, and dbt Cloud.
Data Observability - Data Pipeline Reliability
Data Observability - Data Quality Monitoring


Monitor and ensure data quality as you scale.

Ensure better data quality by monitoring data SLAs, unexpected column changes, and null records before they get to the warehouse. 

  • Track schema changes and column-level statistics.
  • Profile column statistics to manage expectations around your data.
  • Visualize dataset reads and writes in time-based snapshots to understand the impact of unreliable data.
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Databand automatically monitors the health of our Airflow pipelines so we spend less time debugging, and more time building ML models. Before Databand, 60% of our pipelines had at least one data incident. Now less than 1% of pipelines have incidents. This resulted in a 3X increase in our customers since we can now manage our ML deep learning models at scale.

Tzoof Hemed
AI-Engineering Team Leader
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Databand helps us detect data quality issues faster so we can meet our data SLAs. Without Databand, we didn’t know we had problems until two or three days later – forcing us to backfill the data. 

Fithrah Fauzan
Data Engineering Lead
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Databand is helping us achieve better pipeline reliability and higher velocity releases for our data products. The platform saves our team a lot of troubleshooting time by providing one holistic view for job monitoring and dependencies so that everyone can see what’s happening in our pipelines.

Amir Ara
Senior Engineering

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Fix data incidents fast

See how Databand can transform data observability at your organization today.