FROM SOURCE TO USE

Column-Level Lineage

Provide clarity on data origin, transformations, and usage across your landscape.

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Transparent end-to-end lineage.

Triage Data Issues with Precision Using Column-Level Lineage

Our Column-Level Lineage mapping offers deep visibility into your data pipelines, enabling you to trace issues down to the most granular level. By pinpointing the exact origin of data discrepancies and understanding their downstream impact, you can resolve problems faster and more effectively. This level of insight ensures timely resolution and minimizes disruptions across your data workflows.

Manual Lineage to Bridge the Missing Links

Easily define and manage data lineages manually wherever automated tracking may fall short. With Decube, you can establish an approval workflow for data owners, ensuring that every manually defined lineage is accurate and aligned with organizational data governance standards. This flexible approach helps maintain data integrity and fills in gaps for complex or custom data assets.

Impacted Assets in Lineage

When changes occur within a data asset—such as a table, job, or dashboard—the owners of these affected components can easily notify downstream stakeholders. This proactive communication helps downstream asset owners understand the potential ripple effects of the changes, enabling them to take necessary actions to mitigate any disruptions or data quality issues.

Update lineage using API

Engineers now have the flexibility to update data lineage directly through APIs, bypassing the need for manual updates via the user interface. This streamlined approach saves time and enables seamless integration with existing workflows, allowing for faster and more efficient management of data lineage.

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No more firefighting.

Preset field monitors

Choose which fields to monitor with 12 available test types such as null%, regex_match, cardinality etc.

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ML-powered tests for data quality

Thresholds for table tests such as Volume and Freshness are auto-detected by our system once data source is connected.

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Smart alerts

Alerts are grouped so we don't spam you 100s of notifications. We also deliver them directly to your email or Slack.

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Data Reconciliation

Always experience missing data? Check for data-diffs between any two datasets such as your staging and production tables.

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People are loving Decube, see what you are missing

“We have been using this platform for a few months now, and we are extremely satisfied with the results. The platform has helped us to improve the quality of our data and make better business decisions.”

Hendrie | Head - Data Engineering @ Koinworks

“Our company has been using this platform for the past 2 months and it has completely transformed the way we manage our data. The observability features have been especially valuable, allowing us to identify and resolve issues in near real-time”

Simon | Data Engineer @ Flowerchimp

“The data observability features have been a real lifesaver for our organization. We are now able to detect and fix problems in our data pipeline much faster than before.”

Siva | OpsEngineer @ Kollect

Frequently asked questions

What is data lineage and why does it matter?

Data lineage shows the complete journey of data as it flows across different systems—from source to transformation to consumption. It matters because it helps organizations ensure accuracy, trace errors, meet compliance needs, and build trust in their data.

What are the key benefits of implementing data lineage?

Key benefits include improved data quality, faster root-cause analysis, stronger compliance and audit readiness, better collaboration between business and technical teams, and increased confidence in AI and analytics initiatives.

How does data lineage support regulatory compliance?

Regulations like GDPR, HIPAA, and financial reporting standards require organizations to prove where data comes from, how it is transformed, and who has access. Data lineage provides the visibility needed to demonstrate compliance and reduce risk.

What challenges do companies face in tracking data lineage?

Common challenges include siloed systems, manual documentation, evolving pipelines, and incomplete metadata. Automated lineage tools help overcome these challenges by parsing queries, tracking dependencies, and keeping lineage up to date.

What tools or technologies are used for data lineage?

Modern data lineage platforms combine metadata management, query parsing, pipeline integration, and visualization. Advanced solutions (like Decube’s Data Trust Platform) provide end-to-end lineage across hybrid and multi-cloud environments, connecting both technical and business perspectives.

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