Synchronize your Snowflake tags with DataGalaxy
Managing and governing data across multiple platforms can be a daunting task, especially when it comes to maintaining consistency and discoverability.
Tags are essential for categorizing and finding data efficiently, but when tags are not synchronized between systems, it can lead to inconsistencies and data governance issues.
To address these challenges, DataGalaxy introduces a new feature to synchronize Snowflake tags, designed to seamlessly integrate tags bidirectionally between Snowflake and DataGalaxy and ensure smooth and efficient data management.
Tagging defined
Tags are commonly used in data systems, especially in modern data stacks. They’re a powerful tool that can help tackle multiple challenges and can be used by various teams for many purposes. For instance:
- Security teams can add tags to data to protect it using tag-based masking policies
- Financial operations teams use them to follow costs related to data usage
- Data governance teams add tags to data assets to classify them and make them discoverable
This can lead to multiple tags on the same assets, potentially creating confusion in the organization. This confusion can worsen over time if other data systems managed by other teams are integrated, as they could use their own tagging model.
Thanks to DataGalaxy’s bi-directional tags synchronization, those problems are solved:

Users can finally view all their tags from all their systems in the same place, providing a full picture of all tags used by all teams on all data assets

DataGalaxy users can also organize and govern the tags, and define the tagging model that suits the needs of their particular company

Users can collaborate with business data owners to realign the tags on the assets using a user-friendly interface without needing any technical skills
The need for seamless tag integration
In large organizations, data can often be scattered across multiple platforms and systems, each with its own tagging conventions. This fragmentation can lead to several issues:
Inconsistent data governance
When tags are not synchronized, maintaining uniform data governance policies becomes difficult. Different departments may use different tags for the same data assets, leading to confusion and compliance issues.
Difficulty in data discoverability
Without consistent tagging, finding the right data can be like searching for a needle in a haystack. This can slow down decision-making processes and reduce overall productivity.
Increased risk of errors
Manual updates to tags across multiple systems are prone to errors. These errors can propagate through the system, leading to incorrect data usage and analysis.
Using tags in a data catalog is essential for any organization aiming to become more data-driven, as they enhance data discoverability, improve governance, and streamline management.
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Tags provide a standardized way to categorize and access data, ensuring consistent governance across departments and reducing the risk of errors. They also foster better collaboration and facilitate advanced analytics, enabling quicker, more informed decision-making.
By organizing data effectively through tags, organizations can maximize the value of their data assets and drive strategic growth.
Benefits of synchronizing Snowflake tags in DataGalaxy
DataGalaxy’s feature to synchronize Snowflake tags offers a solution to these commonly faced problems, including:
- Bidirectional updates: Automatically sync tags between Snowflake and DataGalaxy, ensuring any changes are reflected across both platforms effortlessly. This eliminates the need for manual updates, saving time and reducing the risk of errors.
- Unified data governance: Maintain consistent data governance by tagging linked objects with the same tags, ensuring all data assets adhere to the same standards and rules. This uniformity is crucial for compliance and for maintaining a high standard of data quality across the organization.
- Enhanced data discoverability: Easily locate and categorize data assets with synchronized tags, improving data accessibility and understanding across your organization.
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This feature allows users to quickly find the information they need, enhancing productivity and decision-making.
DataGalaxy’s bidirectional Snowflake tags feature seamlessly integrates tags from your organization’s Snowflake data source and ensures bidirectional updates. DataGalaxy users can also link objects with the same tags, promoting uniform data governance, enhancing data discoverability, and maintaining consistency across your data assets.
Conclusion
In conclusion, the introduction of DataGalaxy’s Snowflake tag synchronization feature marks a significant advancement in data management and governance.
By enabling seamless bidirectional integration of tags between Snowflake and DataGalaxy, this feature addresses critical challenges like inconsistent data governance, reduced data discoverability, and the risk of manual errors.
By integrating Snowflake tags into DataGalaxy, businesses can ensure a streamlined and efficient data management process, fostering better collaboration and more accurate data governance.
FAQ
- What is DataGalaxy?
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DataGalaxy is a modern data & AI governance platform that centralizes metadata, data lineage, and business definitions to create a shared understanding of data across the organization. Designed for collaboration, we empower teams to find, trust, and use data confidently. Learn how DataGalaxy accelerates data-driven decision-making at www.datagalaxy.com.
- What makes DataGalaxy different?
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DataGalaxy stands out with our user-friendly, collaborative data governance platform that empowers everyone—from data stewards to business users—to understand, trust, and use data confidently. Unlike complex legacy tools, DataGalaxy offers intuitive metadata management, real-time lineage, and a business glossary in one centralized hub. Discover how we drive agile, value-first data strategies at www.datagalaxy.com.
- What is data quality management?
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Data quality management ensures data is accurate, complete, consistent, and reliable across its lifecycle. It includes profiling, cleansing, validation, and monitoring to prevent errors and maintain trust. This enables smarter decisions and reduces risk.
- What is reference data?
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Reference data categorizes other data—like country or currency codes—and provides a stable framework for consistency across systems. Proper management supports data quality, compliance, and operational efficiency by ensuring accurate, reliable reporting and analysis.
- What is reference data management?
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Reference data management oversees classifications like country codes or product categories across systems. Since it’s widely shared, consistency and accuracy are essential. Centralized management boosts efficiency, ensures compliance, and supports better decisions through a unified view of key business terms.