Continuous Data Observability Platform
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data observability.
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Databand is trusted by the modern data teams to achieve better data quality for their business. Whether it’s detecting broken data pipelines or data quality issues at-rest in your warehouse, Databand has you covered.
Top US banks use Databand
Average improvement of mean time to resolution
Data quality monitoring for pipelines and at-rest
Supported integrations for data observability
Detect data incidents early, resolve them fast, and deliver trustworthy data.
Pinpoint unknown data incidents, and reduce mean time to detection (MTTD) from days to minutes.
Improve mean time to resolution (MTTR) with incident alerts and routing from weeks to hours.
Ensure confident decision-making for business consumers and keep customers happy.
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.
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.
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.
Let’s face it. There are a lot of data observability solutions out there. Here’s why Databand’s “shift-left” approach is different. Unlike others that only monitor data-at-rest in your warehouse, Databand provides a continuous data observability approach that ties directly into all stages of your data lifecycle, starting with your source data.
Continuous Observability
Reactive Observability
Databand removes bad data surprises by detecting and resolving them before they create costly business impacts.
Automatically collect metadata from your modern data stack like Airflow, Spark, Databricks, Redshift, dbt, and Snowflake.
Build historical baselines based on common data pipeline behavior and get visibility into every data flow, from source to destination.
Detect high severity data reliability errors that impact your most critical pipelines and alert impacted teams.
Create smart communication workflows to resolve data quality issues & meet SLAs.
Databand’s open-source library enables you to track data quality information, monitor pipeline health, and automate advanced DataOps processes. We keep our library open to give engineers complete control over tracking data and the resources needed to build custom extensions.
Improve data reliability and quality under one roof with a single pane of glass for all your data incidents.
Visualize how data incidents impact upstream and downstream components of your data stack.
Monitor data pipeline errors such as failed runs, longer than expected durations, missing data operations, and unexpected schema changes.
Continuously validate data quality with dataset metrics for SLAs, column changes, and null records.
Eliminate the unknown by seeing trends & detecting anomalies from your metadata in real-time.
Customize incident alerts and route notifications to impacted DataOps teams for faster resolution.
Databand’s combined capabilities provide a one-stop-shop for all your data incidents. Now platform and data engineers can focus on building, not fixing, their modern data stack.
See how Databand can transform data observability at your organization today.