To detect bad data quality issues, IBM® Databand® provides real-time data quality monitoring you can trust.
As problems with data often lie below the surface, data engineering squads understand they must do more than simply execute data pipeline runs from one point to the next. That said, because data deliveries contain thousands of rows and values, it’s common for delays, poor quality and volatility within the data itself to go overlooked.
Data quality monitoring with IBM Databand connects to your data pipelines and datasets to alert on problems like schema changes, duplicates, null values and data freshness. It also gives you the ability to visualize datasets over time, so you can analyze trends and find patterns in data quality that require immediate attention.
Databand notifies you when column-level changes, value irregularities or other profiling anomalies occur so you can ensure better quality data.
By setting rules for data freshness, you always know when a dataset hasn’t been updated within a service level agreement (SLA) timeframe.
Databand helps identify dependent tasks and datasets when a data quality error occurs so you have full transparency and can prioritize remediation.
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.