For the past 12 years, Snowflake has been making waves in the data world, promising to bring enterprise AI to the masses.

With its array of powerful tools for processing and querying data, Snowflake sets the stage for businesses to unlock their data’s potential. But behind the AI-powered dashboards and the natural language queries lies a critical question: Who manages the semantic layer that makes all of this work seamlessly?

Snowflake provides the technology to process and store data, but the semantic layer — The piece that makes data meaningful, accessible, and consistent — enables users to truly harness the platform’s power. This is where DataGalaxy takes on the essential but often overlooked task of creating, managing, and optimizing the semantic layer.

Keep reading to learn more about what goes into using a semantic layer with Snowflake tools.

Why the semantic layer matters

The semantic layer turns raw data into understandable insights - It’s the link between technical schemas and business-friendly terms, acting as a translator that ensures everyone in an organization, from analysts to decision-makers, can make sense of the data.

A semantic layer adds additional understanding to Snowflake’s complex platforms. Natural language processing (NLP), AI insights, and advanced queries depend on a well-defined structure to ensure data accuracy and usability. The semantic layer:

• Defines business terms: It ensures that everyone uses the same definitions for key metrics and dimensions, like “customer churn” or “revenue”
• Adds context to data: It organizes raw data into meaningful concepts, bridging the gap between technical complexity and business relevance
• Supports governance: It helps maintain data quality, traceability, and compliance, ensuring data is reliable and trusted
• Simplifies access: It allows non-technical users to interact with data through intuitive, no-code tools

In short, the semantic layer isn’t just a behind-the-scenes feature; it’s the foundation for effective AI and analytics.

Snowflake’s & DataGalaxy’s roles in data curation

Snowflake excels at high-performance data storage, processing, and querying. With features like Snowflake Cortex Analyst and its YAML-based specifications for semantic modeling, Snowflake provides the tools needed to build a semantic layer.

However, the actual work of defining, managing, and enriching that layer often requires external solutions.

DataGalaxy doesn’t just support Snowflake’s capabilities; it supercharges them by simplifying the semantic modeling process and making data more accessible and actionable for users.

DataGalaxy is pushing the semantic layer forward

DataGalaxy has built its reputation around helping organizations organize and manage their data. Its platform is specifically designed to handle the complexity of creating semantic layers, enabling businesses to focus on insights rather than infrastructure.

Here’s how DataGalaxy is leading the way:

Why DataGalaxy is essential for well-rounded Snowflake AI use

Snowflake’s AI capabilities are only as good as the supporting data structure. A poorly defined or inconsistent semantic layer can lead to confusion, errors, and missed opportunities. DataGalaxy eliminates these risks by making the semantic layer a well-oiled machine.

Consider this example: A business user wants to know, “Which customers increased their spending in 2023?” With DataGalaxy, the semantic layer ensures this query is mapped to the correct tables, joins, and metrics in Snowflake, delivering accurate results every time.

Conclusion: The real power behind enterprise AI

Snowflake’s promise to “Bring enterprise AI to you” is undeniably exciting. However, AI doesn’t run on raw data alone. It needs a semantic layer to make that data usable, understandable, and actionable.

While Snowflake provides the infrastructure, DataGalaxy ensures that a data team’s infrastructure is built on a solid foundation. By simplifying semantic modeling, standardizing business definitions, and making data accessible to all, DataGalaxy empowers organizations to unlock the full potential of Snowflake’s AI capabilities.