To remove bad data surprises from your data pipelines and datasets, IBM® Databand® delivers automatic anomaly detection.
Today’s data platform teams are often reactive when data reliability and quality incidents occur, which are typically found by downstream consumers hours or even days later. The data engineering team is blamed, and the damage done can take months to fix.
IBM Databand features machine learning (ML)-powered anomaly detection to immediately flag when a data incident happens. It builds a historical baseline from your data stack’s metadata and uses smart alerting workflows when operations deviate from the baseline. You miss nothing and can act fast.
Try an interactive product tour of Databand to see how easy it is to create and debug data incident alerts and get started with dashboards and reports.
Detecting data anomalies from your metadata in real time builds analysts’ trust in their findings, leading to better-informed decisions and improved outcomes.
Out-of-the-box and customizable trigger alerting helps detect anomalies 24/7 and ensure that all data delivered is accurate and ready for consumers.
Exploring historical pipeline data allows engineers to retrospectively investigate anomalies so they can optimize future pipeline performance.
Databand integrates with the data pipeline and integration tools you already use and love, like Apache Airflow and IBM® DataStage®, for continuous data observability across your data fabric and modern data stack.