IBM Databand is a data observability platform that automatically collects metadata from data pipelines and warehouses to establish historical baselines and detect anomalies. It enables teams to identify data quality issues early and implement remediation workflows, ensuring reliable data delivery and minimizing disruptions.
Funding
$14.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Data pipelines and warehouses can suffer from data quality issues such as anomalies, unexpected changes, and pipeline failures, leading to unreliable data delivery and potential disruptions for downstream consumers. Identifying these issues early and implementing effective remediation workflows is critical for maintaining data integrity.
Solution
IBM Databand is a data observability platform designed to automatically collect metadata from data pipelines and warehouses, establishing historical baselines and detecting anomalies. The platform provides data teams with immediate visibility into their data landscape, enabling them to proactively identify and resolve data quality issues. By monitoring data SLAs, unexpected column changes, and null records, Databand helps ensure data reliability and maintain data team confidence. The platform facilitates the creation of smart communication workflows to remediate data quality issues and keep data deliveries on track.
Target Audience
The primary users are data engineers, data scientists, and data platform teams responsible for ensuring the reliability and quality of data pipelines and warehouses.
Features
- Automated metadata collection for immediate visibility into data pipelines and warehouses.
- Historical baseline creation based on common run and data behaviors.
- Anomaly detection and alerting on deviations from established baselines and rule breaches.
- Data incident management dashboard for seeing, responding to, and resolving issues without delay.
- Data pipeline monitoring to detect and manage missing operations, failed jobs, and run durations.
- Data quality monitoring to alert on data SLAs, unexpected column changes, and null records.
- Data lineage and impact analysis tools to understand the impact of data incidents on all flows.