Data Observability 101: An Introduction to the Most Critical Features of Modern Data Observability

Data observability has become a critical aspect of modern data management as companies increasingly rely on data pipelines for their products or decision-making processes. Similar to supply chain management, data observability involves understanding the health and state of data across fragmented ecosystems. It can be broken down into three basic functions: analytics, monitoring, and alerting. Analytics focuses on the performance, status, and relationships between dependencies in a data product, such as tasks and datasets. Monitoring concerns data quality checks, task and pipeline run duration tracking, run status checking, and service-level agreements (SLAs) for tracking data freshness and timeliness. Alerting extends beyond reactive alerting to encompass proactive alerting, using analytics and monitoring to trigger alerts when anomalies are detected. Data observability is essential for companies to protect their critical data supply chains and enable teams to root-cause analyze issues quickly. It requires insight into who owns what up and down the supply chain, historical and predictive analytics for asset health, and data lineage tracking. Astro Observe, a new observability product, offers unified analytics, monitoring, and alerting features to help organizations manage their data pipelines effectively.

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