Data Catalog for Snowflake: Buyer's Guide

Business owners know by now: good data management is good for business. Oganizations are increasingly turning to advanced platforms like Snowflake to manage and analyze their vast amounts of data. As businesses leverage Snowflake's powerful cloud data warehousing capabilities, the need for effective data management tools becomes paramount. 

A data catalog serves as a critical component, offering comprehensive data governance, discovery, and optimization features that enhance the overall functionality of Snowflake. This buyer's guide explores the key benefits of implementing a data catalog with Snowflake, its role in cloud migration, and the unique differences between Snowflake's Polaris catalog and established data catalogs. Read on to discover how a data catalog can unlock the full potential of your Snowflake investment.

The rise of Snowflake

Snowflake has rapidly become a dominant force in the world of cloud data warehousing, offering a robust platform for data storage, processing, and analytics. Founded in 2012 by Benoit Dageville, Thierry Cruanes, and Marcin Zukowski, Snowflake was designed from the ground up to leverage the power of the cloud. The company's innovative architecture separates storage and compute, allowing for scalable, on-demand access to data without the performance issues typically associated with traditional data warehousing solutions.

Since its inception, Snowflake has grown exponentially, attracting a wide array of customers across various industries due to its flexibility, scalability, and ease of use. The platform supports diverse workloads, from data warehousing and data lakes to data engineering and data science, making it a versatile choice for modern enterprises looking to harness their data effectively.

Benefits of implementing a data catalog with Snowflake

According to Dave Wells of Eckerson Group, “A data catalog is a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate the fitness of data for intended uses.” Today, many data catalogs are repositioning their product as a data intelligence platform, a phrase which better describes the value of such a platform. 

A data catalog supports Snowflake users in the following key ways:

• Enhanced data discovery: A data catalog provides a centralized repository where users can easily find and understand the data available within Snowflake – and across the entire data ecosystem. This accelerates the process of locating relevant data sets, reducing the time spent searching for information.

• Improved data governance: With a data catalog, organizations can enforce and scale data governance policies more effectively. The catalog enables better tracking of data lineage across tools, supporting compliance with regulatory requirements while maintaining data integrity.

• Streamlined collaboration: Data catalogs facilitate collaboration among data analysts, scientists, and engineers by providing a shared platform for accessing and annotating data. This improves communication and fosters a data-driven culture within the organization.

• Increased data quality: By documenting data sources, transformations, and usage, a data catalog helps identify and rectify data quality issues. Some catalogs that offer integrations with data quality vendors will surface health metrics to end users, making it easier to find trusted data or avoid low-quality data. This leads to more reliable and accurate analytics.

• Optimized cloud resources: A data catalog can help monitor and manage data usage within Snowflake, enabling organizations to optimize their cloud resources and reduce costs.

By activating metadata across the modern data stack, a data catalog can deliver useful, curated intelligence about data assets to data newcomers, making it easier for that data to be found, understood, governed, and used efficiently. For this reason, implementing a data catalog with Snowflake brings numerous benefits to organizations aiming to enhance their data management and analytics capabilities:

How a data catalog supports cloud migration

Many organizations approach Snowflake with the goal to modernize their data landscape and integrate the cloud data warehouse into their workflows. This demands a cloud migration strategy. Leaders should keep in mind that, according to Gartner, 83% of data migration projects fail or exceed their budgets and schedules. 

This is because migrating data to the cloud is a complex process that requires careful planning and execution. A data catalog can play a pivotal role in supporting cloud migration efforts by providing the following benefits:

• Comprehensive inventory: A data catalog offers a detailed inventory of all data assets, and can even spotlight your most used (and useful) data. This makes it easier to assess which data sets need to be migrated and in what order. It also ensures that no critical data is overlooked during the migration process.

• Data lineage tracking: Understanding the lineage of data is crucial during migration. A data catalog tracks the origins, transformations, and destinations of data, providing a clear picture of how data flows through the organization. This information is invaluable for ensuring data integrity during the migration (and avoiding breaking dependent legacy assets – more on this below).

• Dependency management: Data catalogs help identify dependencies between data sets and applications. This knowledge is essential for planning the migration sequence and minimizing disruptions to business operations.

• Risk mitigation: By offering insights into data quality and governance (such as the location of PII data), a data catalog helps mitigate risks associated with data migration. It ensures that data remains accurate and compliant with regulatory standards throughout the process.

• Post-migration optimization: After migration, a data catalog continues to provide value by helping organizations optimize their cloud data architecture. It enables ongoing monitoring and management of data assets, ensuring that the cloud environment remains efficient and cost-effective.

Implementing a data catalog with Snowflake is a strategic move that brings multiple benefits, from enhancing data discovery and governance to fostering collaboration and improving data quality. By centralizing and organizing data assets, a data catalog enables organizations to maximize the value of their Snowflake investment. Ultimately, this leads to more informed decision-making, greater operational efficiency, and optimized cloud resource utilization.

Snowflake Polaris Catalog versus established data catalogs: What's the difference?

Snowflake recently launched Polaris, which has sparked interest and some confusion within the industry. It's important to clarify that Polaris is not a traditional data catalog like those offered by Alation or Collibra. Instead, Polaris is designed to facilitate query engines' access to data stored in Iceberg tables, serving more as a tool for interoperability rather than data discovery or governance.

Rather, Polaris is akin to Databricks' acquisition of Tabular, aimed at preventing competition between Iceberg and Delta Lake. Similarly, Snowflake's Polaris ensures that data can be queried using various engines, even if it means supporting competitors.

For those seeking robust data governance features, Snowflake Horizon is the more relevant offering. Horizon integrates with Polaris but extends its capabilities to include comprehensive data governance, making it more comparable to traditional data catalogs.

Key differences

• Polaris: Focuses on enabling query engines to read data in Iceberg tables. It's not a comprehensive data catalog for discovery or governance.

• Horizon: Offers data governance features, integrating with Polaris to provide a complete solution for managing and securing data.

• Alation: Data catalog (or data intelligence platform) focused on data discovery, governance, and collaboration. This platform provides extensive metadata management and support for complex data environments.

Key data catalog use cases for Snowflake

Implementing a data catalog with Snowflake unlocks several valuable use cases that can enhance an organization's data management and analytics capabilities:

Automate policies

Automating policies with a data catalog ensures that data governance rules are consistently applied across the organization. This includes access controls, data retention policies, and compliance requirements. By leveraging automation, organizations can reduce the risk of human error and ensure that data governance practices are uniformly enforced.

Leverage data lineage

Data lineage provides a detailed map of data's journey from its source to its final destination. With a data catalog, organizations can easily trace data lineage, gaining insights into how data is transformed and used. This transparency helps in troubleshooting data issues, auditing data usage, and ensuring compliance with regulatory standards.

Democratize SQL with an Intelligent SQL Editor

An intelligent SQL editor integrated with a data catalog can democratize data access by enabling users with varying levels of SQL proficiency to query data efficiently. Features like autocomplete, syntax highlighting, and query optimization tips help users write accurate and efficient queries, democratizing access to even non-technical users.

Optimize cloud costs with consumption tracking

Tracking data consumption is crucial for optimizing cloud costs. A data catalog can provide detailed insights into how data is being used, helping organizations identify areas of inefficiency and opportunities for cost savings. By monitoring data usage patterns, organizations can make informed decisions about resource allocation and cloud infrastructure management.

Conclusion

As organizations increasingly rely on Snowflake for their data warehousing and analytics needs, integrating a data catalog becomes essential for maximizing the platform's potential. A data catalog not only enhances data discovery and governance but also supports critical processes like cloud migration and cost optimization. 

Understanding the differences between Snowflake's Polaris and traditional data catalogs ensures that organizations can choose the right tools for their specific needs. By leveraging the key use cases outlined in this guide, businesses can harness the full power of their data, driving innovation and achieving their strategic objectives.

Curious to learn how Alation can help you make the most of Snowflake? Book a demo with us to learn more.