Understanding the journey of your data across different systems and technologies is essential for effective data management and governance. However, tracking data lineage can be complex and time-consuming, especially when dealing with diverse data sources.

DataGalaxy now offers cross-technology automated column-level data lineage in collaboration with Snowflake, providing a comprehensive view of your data’s path.

The need for automated column-level lineage

Data lineage tracking plays a critical role in modern data management by offering valuable insights into the life cycle of data from its origin, transformations, and eventual destination.

Automated data lineage tools visually map the journey of your data from source to destination. These tools simplify regulatory compliance, migration planning, root cause analysis, and impact analysis.

However, organizations may face several challenges in tracking data lineage, including:

  • Lack of visibility: Without a clear view of how data flows through various systems, it’s difficult to understand the full context and origin of data. This lack of visibility can lead to data quality issues and hinder decision-making.
  • Complex integration: Data often moves through multiple platforms and technologies, each with its own tracking mechanisms. Integrating these diverse systems to provide a cohesive view of data lineage is a complex task.
  • Data silos: Different departments may use isolated systems, leading to data silos. These silos prevent effective data sharing and collaboration, limiting the overall efficiency of the organization.

The benefits of cross-technology automated column-level lineage

Establishing a trusted source of truth is essential for any organization looking to organize, standardize, and share its data assets among the entire organization. Using a detailed exploratory data lineage visualization tool is essential to help business and technical users alike understand data flows, relationships, and health to enhance decision-making across the entire organization.

DataGalaxy’s cross-technology automated column-level lineage addresses the common data lineage issues by providing the following tools:

  • Uniform Resource Names (URNs): DataGalaxy assigns a unique URN to every object connected to Snowflake, simplifying verification and information management. The URN acts like a digital fingerprint, ensuring that each data asset can be easily identified and tracked.
  • Improved connectivity: The new API support enhances connectivity and information sharing across different DataGalaxy connectors, allowing easy tracking of data lineage. This feature breaks down barriers between systems, facilitating smoother data flow and integration.
  • Breaking down silos: DataGalaxy standardizes data lineage tracking, facilitating better collaboration and data sharing, and ultimately leading to more informed decision-making. By understanding the flow of data, organizations can optimize their processes and ensure that all teams are on the same page.

Conclusion

In conclusion, the integration of cross-technology automated column-level lineage through DataGalaxy and Snowflake marks a significant advancement in data management and governance.

By addressing key challenges such as lack of visibility, complex integration, and data silos, DataGalaxy and Snowflake provide organizations with a comprehensive and unified view of their data’s journey across diverse systems. DataGalaxy’s unique identifiers, improved connectivity, and standardized tracking not only simplify data management but also enhance collaboration and decision-making for the entire organization.

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Are you interested in learning even more about using your data as an asset to achieve higher levels of data governance and data quality? Book a demo today to get started on your organization’s journey to complete data lifecycle management with DataGalaxy.