Metaplane Announces Column-Level Lineage Visualization for Customers

Metaplane, a data observability platform, has unveiled a new column-level lineage map. The visualization gives their customers a bird’s eye view of their data environments, which empowers users to prevent data issues, diagnose data anomalies, and ensure full coverage for their data quality monitoring.

In modern companies, data often flows through a complex stack of software. This stack includes warehouses that store and process data, tools that transform and move the data, and business intelligence (BI) software to view the data. This proliferation of tools makes it challenging to get a complete view of how data gets from point A to point B. But Metaplane, which integrates with several commonly-used data tools, is perfectly positioned to give users those insights into their data.

Preventing Data Issues

Traditionally, data engineers who wanted to make a change to their data environment had to spend hours or days tracking down every table or dashboard that might be impacted. Now, with Metaplane’s new lineage map, users can easily see everything that sits “downstream” of a column or table. This insight helps give data engineers confidence that they know exactly how their change will affect the rest of their data ecosystem. In the case of a data quality incident, the visualization also gives users insight into dashboards that may be displaying incorrect or incomplete data.

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Diagnosing Data Anomalies

Data anomalies frequently affect several tables or columns at once, as the anomaly cascades through the data environment. By using Metaplane’s lineage map, data engineers can quickly pinpoint which upstream table or column first experienced that data anomaly. Identifying an issue’s root cause can save hours of time and effort for data engineers who must quickly triage and troubleshoot the incident.

Ensuring Full Monitor Coverage

Metaplane’s lineage map provides helpful hints so users can understand which parts of their data environment have monitoring and which do not. By identifying columns or tables that are heavily used by other tables and BI dashboards, users can make sure they are the first to know about any data quality issues.

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