
Snowflake brings enterprise AI, but who’s pushing the semantic layer?
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.
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Discover the data catalogSnowflake’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:

Streamlining semantic modeling
DataGalaxy makes it easy to define and structure the semantic layer by leveraging existing glossaries, data dictionaries, and lineage tools.
Instead of starting from scratch, organizations can build their semantic models using pre-existing business terms and data relationships.

Ensuring consistency
With DataGalaxy, businesses can standardize how data is defined and used across teams.
Whether it’s defining dimensions like “Customer region” or measures like “Profit margin,” DataGalaxy ensures that everyone is speaking the same language.

Empowering non-technical users
Not everyone in an organization knows SQL – Or even needs to.
DataGalaxy bridges the gap, allowing business users to interact with data through intuitive tools and natural language interfaces, all while relying on a robust semantic layer.

Enhancing governance & trust
DataGalaxy’s lineage tools provide complete transparency so users can trace every metric and dimension back to its source.
This ensures compliance, trust, and accountability in data-driven decisions.

Accelerating AI-driven insights
By organizing and enriching the semantic layer, DataGalaxy enables AI models and NLP tools to perform more effectively.
Users can ask questions in plain language and receive accurate, actionable answers thanks to a well-structured semantic layer.
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.
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Save your seatConsider 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.
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.
FAQ
- What’s the difference between data governance and data management?
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Data management is the operational handling of data (storage, processing, integration), while governance defines *how* and *why* it should be managed — including standards, responsibilities, and policies. Governance is the strategy layer.
👉 Want to go deeper? Check out:
https://www.datagalaxy.com/en/blog/data-governance-and-data-mesh/ - What is a business glossary?
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A business glossary is a centralized repository of standardized terms and definitions used across an organization. It ensures consistent language, improves communication, and aligns teams on data meaning. Essential for data governance and compliance, a business glossary boosts data quality, reduces ambiguity, and accelerates AI and analytics initiatives with trusted, shared understanding.
- What is a Chief Data Officer?
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A Chief Data Officer (CDO) ensures data is well-managed, trusted, and drives business value. They lead data strategy, governance, and quality, helping teams turn data into actionable insights.
- 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 from other data catalog solutions?
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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.