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Overcoming Data Lineage Challenges
As a data expert, I share strategies for tackling data lineage challenges. Learn how to maintain trustworthy data and prevent system failures.
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Introducing Decube’s Copilot: Smarter Data Curation
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Provide clarity on data origin, transformations, and usage across your landscape.
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.
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.
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.
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.
Choose which fields to monitor with 12 available test types such as null%, regex_match, cardinality etc.
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Thresholds for table tests such as Volume and Freshness are auto-detected by our system once data source is connected.
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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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Always experience missing data? Check for data-diffs between any two datasets such as your staging and production tables.
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Decube derives data lineage using a proprietary algorithm that automatically scans and deciphers relationships between tables, pipelines, and datasets. By analyzing metadata, we can trace the flow of data across systems, ensuring complete visibility into your data infrastructure.
Decube parses query and pipeline logs to understand the underlying flow of data. By examining execution plans, we capture relationships between tables, databases, and even cloud-based data lakes. This enables us to handle complex ETL and ELT pipelines efficiently.
Our proprietary algorithm goes beyond traditional lineage tracking by parsing both SQL queries and pipeline logs, offering deeper insights into the entire data flow. This includes visibility into cloud storage, data lakes, and multi-cloud environments, making us uniquely suited to modern data architectures. Along with this, we do provide manual lineage addition right from the platform with an approval workflow.
Yes, Decube’s system supports real-time monitoring and updates, providing dynamic lineage tracking as data changes and flows through the pipeline. This is especially useful in fast-paced environments where data is continuously being ingested and transformed.
Yes, Decube allows users to customize and filter lineage views based on their specific needs. You can track data flows at table, column, or pipeline levels, offering granular insights that match your operational requirements.
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