IBM Databand

Databand provides a single pane of glass so as to see data incidents in one place for immediate detection and faster resolution. Users can access centralized alerts and notifications on issues that require immediate attention. And set alerts on all integrated tracked metadata—from run durations to data quality metrics, as well as notify the right person to fix a data issue before it impacts other data workflows. Additionally, users can catalog and profile all alerts under one roof across the data stack, and integrate to popular data engineering tools like Apache Airflow, Spark, and dbt Cloud.


data pipeline monitoring
Databand connects to an enterprise's data processes and pipelines to detect incidents like missing operations, failed jobs, and run durations so users can handle pipeline growth, and monitor pipelines for early warning signals of failures or missed SLAs. Databand also provides visibility across DAGs, data flows, and levels of infrastructure for better pipeline reliability. This helps users know exactly how to fix pipelines with automatic notification management, logging, and lineage.


data quality monitoring
Databand helps to ensure better data quality by monitoring data SLAs, unexpected column changes, and null records before they get to consumers. Databand detects bad data quality while it’s in motion, catching data schema and data profiling anomalies, such as null counts, type changes, and skews. And prevent data failures before they reach downstream consumers and production tables.


anomaly detection
Data anomaly detection removes any bad data surprise by automatically detecting deviating behavior in data pipelines and datasets, to eliminate the unknown by seeing trends & detecting anomalies from metadata in real-time. Users can set alerts to trigger when unexpected data deviates from expected baselines immediately. Databand monitors and detects anomalies 24/7 to help ensure accurate delivery.


end-to-end data lineage
End-to-end data lineage helps understand the impact of data incidents on upstream and downstream data flows. It helps save debugging time by automatically knowing what processes were impacted by the incident, and it makes it easier to prioritize data incidents better by knowing which alert is causing the most corruption downstream.