A data catalog provides an organized way to manage and govern data across different platforms. Without it, data becomes disorganized, which leads to poor decision-making. In response, many organizations are adopting new technologies like Snowflake to process their extensive data.
Snowflake is a cloud-native architecture that allows scalable data warehousing. A single organizational account can hold multiple databases with thousands of tables. Managing these assets efficiently becomes problematic when different teams run queries and analyze data.
The Snowflake data catalog solves this problem by providing an organized way to track relationships between tables and views and identify frequently accessed data.
What is a Snowflake data catalog?
A Snowflake data catalog is a cloud-native tool that organizes and manages metadata within the Snowflake platform. Unlike traditional data catalogs, which are designed for on-premise systems, Snowflake’s catalog is optimized for cloud environments. It stores metadata that helps users easily locate and understand their data so they can manage it more efficiently in a dynamic cloud environment.
Benefits of using a data catalog in Snowflake
Snowflake data catalog provides many benefits for data storage over traditional data catalogs. Here are a few of them:
Enhanced data discovery: Snowflake has a user-friendly interface. You can use features like keyword search and category filters to reduce the time spent searching.
Improved data governance and compliance: Snowflake's tools manage data access without compromising quality. Organizations can also set rules for access controls and maintain data usage logs to comply with regulations like GDPR.
Increased data quality and accuracy: In Snowflake, teams can trace data sources and observe how data changes, which helps them confirm that they're using reliable information.
Efficient data management: In Snowflake, users can organize data assets in a centralized view so data teams can maintain and update information when it's out of date.
Support for advanced analytics and machine learning: Data scientists find and validate datasets for their models in Snowflake, by understanding the data's history and characteristics before use.
Evaluating data catalog tools for Snowflake
Here’s what you should consider when you're evaluating data catalog tools for Snowflake:
Integration capabilities
A strong connection between your data catalog and Snowflake is important for success. The tool you choose should automatically sync with Snowflake to ensure that any changes in your data warehouse are immediately reflected in the catalog.
Good integration means you won't need constant manual updates, and your team can trust that the catalog shows up-to-date information. Look for tools that offer native Snowflake connectors and require minimal setup time.
Metadata management
Your data catalog should capture key information about your data assets; things like column descriptions, data types, update frequencies, and usage statistics. The best tools will automatically scan your Snowflake environment to detect changes and maintain historical records.
Data discovery
A good data catalog makes finding the right data simple and quick. You should be able to search across all data assets using plain language, similar to using a search engine. The data catalog should offer smart filtering options, like searching by data type, owner, or department. In addition, look for features that help organize data meaningfully, like custom tags, business glossaries, and the ability to create data collections.
Data lineage
How data moves and changes has a huge impact on maintaining trust and quality. A good data catalog creates clear visual maps to show where data originates, how it transforms, and where it ends up being used. This helps teams track down data quality issues and assess the impact of potential changes.
Security and compliance
Your data catalog must have strong security features to protect sensitive information. That’s why look for tools that integrate with your existing security systems and offer fine-grained access controls.
You should be able to set different permission levels for different users and teams. The tool should also maintain detailed audit logs of who accessed what data and when. Features like automatic data masking for sensitive fields and compliance reporting will help you maintain security standards.
Collaboration and knowledge sharing
A data catalog should make it easy for teams to work together with data. Users should be able to add descriptions and usage examples to data assets. The tool should allow teams to share datasets and insights safely while respecting access permissions.
Scalability and flexibility
As your organization grows, your data catalog should grow with you. It should handle increasing data volumes without slowing down or becoming unstable. The catalog should be flexible enough to adapt to new types of data and changing business needs.
Consider whether the tool can scale across multiple departments or regions while performing well. The best tools offer ways to automate more processes as your data environment becomes more complex.
Snowflake's native data catalog vs. third-party solutions
Snowflake's native data catalog provides built-in data management and governance features. On the contrary, third-party solutions may offer additional functionalities and flexibility but often require more complex integration. So, you should weigh the benefits of native capabilities against the specialized features of external tools to determine what best meets your data needs.
Overview of Snowflake's native data catalog
Snowflake's native data catalog includes features like Object Tagging and Data Classification, which help users categorize and label data based on specific criteria. Recently, Snowflake also introduced the Polaris Catalog, which works specifically with Apache Iceberg tables. Polaris is open-source and works well with different data tools, not just Snowflake's own products.
Core features of Polaris catalog
Polaris catalog is designed to be flexible and user-friendly. Here are its three main features:
Cross-engine read and write operations: Allows different data processing units to work together on the same data. This way, it diminishes the need to move or duplicate data.
Centralized access to iceberg tables: Gives a single point of access to manage and organize iceberg tables which simplifies the data management.
Integration with snowflake horizon: Scales data governance and security by connecting with Snowflake horizon for improved data management controls.
Avoid vendor lock-in: Organizations can use various tools and platforms, like AWS, Azure, or Google Cloud, without being tied to one vendor.
Limitations of Snowflake's native catalog
While Snowflake's native catalog handles basic needs well, it has some limitations. It doesn't offer advanced features like detailed data lineage tracking (showing how data moves and changes over time) or a business glossary (a shared vocabulary for your data).
For companies with complex data needs, these missing features may be important. That’s why this basic catalog works for simple setups, but larger organizations may need more capabilities.
Advantages of third-party data catalog solutions
Third-party data catalog tools like data.world often provide more extensive features than Snowflake's built-in option. They offer:
Better metadata management
More detailed data lineage tracking
Broader integration with different data sources
AI-powered data discovery to help find information faster
Automatic data quality checks
Better tools for team collaboration
These external tools can be especially helpful for organizations that use many different data sources and need more advanced features than what Snowflake provides on its own.
Best practices for implementing a Snowflake data catalog
Here are some best practices to follow when implementing a Snowflake data catalog:
Document everything: Create standardized documentation for all datasets that include business descriptions, ownership details, update frequency, and data lineage. Use clear naming conventions and ensure descriptions are business-friendly rather than overly technical.
Establish a strong governance framework: Implement role-based access control and regular security reviews to maintain data integrity. This can include tracking and resolving quality issues and maintaining clear audit trails of catalog changes.
Build a collaborative environment: data.world creates an environment where data discovery and collaboration go hand in hand. It enables features that allow teams to provide feedback and receive notifications about important dataset changes. This transparency ensures the catalog remains accurate and valuable while fostering cross-team collaboration.
Maintain and monitor usage: Track how teams use the catalog and integrate catalog maintenance into standard workflows. This will help you identify areas for improvement and promote a data-driven culture across the organization.
data.world's Snowflake data catalog
data.world's seamless integration with Snowflake transforms how organizations manage their data assets. It automatically scans and catalogs metadata so teams can quickly find and understand available datasets. Through its user-friendly interface, your teams can collaborate effectively by sharing insights and maintaining governance standards all in one place.
As your data ecosystem grows, data.world scales alongside to make sure governance stays strong without sacrificing accessibility. Every feature, from the automated metadata management to the business glossary, is designed to make enterprise-grade data management straightforward and compliant.
Ready to explore more? Schedule a demo today and see how data.world can change your data management practices.
