Honeydew
A Semantic Layer Native to Snowflake

Analysts among other users require data metrics to perform their tasks, but what if there’s no data consistency when it’s accessed from different tools? Cloud-based platforms like Snowflake provide powerful data solutions where businesses consolidate their data. However, organizations often encounter data interpretation and usage discrepancies when integrating with various tools. These inconsistencies can lead to confusion and hinder effective decision-making. Moreover, businesses rely on tools like Excel, Tableau and Power BI for data access which struggle to handle the load of more complex data without moving it out of Snowflake.

A semantic layer is essential to address these challenges, providing a standardized framework that ensures all users have a common understanding of the data.

This is where Honeydew comes in, offering a semantic layer native to Snowflake that bridges the gap between Snowflake and the myriad of tools businesses use, enhancing data accessibility and ensuring reliable insights.

“Our approach provides centralized, consistent metrics across the organization. We empower users from analysts using a BI tool to business users assisted by AI with access to the shared knowledge of their business,” says David Krakov, co-founder and CEO of Honeydew.

Apart from these immediate gains, Honeydew has long-term advantages as well. Data centralization improves team collaboration and cost management and streamlines performance.

A single place to define business metrics also helps with version control, code reviews and testing processes.

Our approach provides centralized, consistent metrics across the organization. We empower users from analysts using a BI tool to business users assisted by AI with access to the shared knowledge of their business

Honeydew can achieve all this and more by bridging Snowflake and the data tools. Honeydew enables them to work directly on Snowflake without data replication, ensuring real-time efficient access to large and complex datasets. The easy access allows users to ask more complicated queries without relying on data engineers for custom pipelines or data exports.

The bridging also creates a single source for metric definitions, providing the various tools or even AI with data consistency, eliminating the need for manual management.

The consistency, coupled with its nativity to Snowflake, enables Honeydew to integrate with any tool connected to Snowflake, unlike legacy systems from Microsoft and SAP that limit users to their respective ecosystems. This flexibility means businesses can use their preferred BI tools, making it user-friendly. It also doesn’t require technical expertise in complex coding for users to work with data models, making it more accessible across teams.

Honeydew even allows AI systems like Cortex and ChatGPT to understand and respond to business queries using the same data definitions that BI tools rely on. AI opens up many opportunities, but the result must be consistent, explainable and trustworthy for success, which is possible thanks to Honeydew.

An instance where Honeydew’s approach changed the trajectory for a business was with a client that had to copy large datasets from Snowflake daily into Microsoft Fabric, limiting the speed and scope of data access for their finance team. After implementing Honeydew on top of Snowflake, the team could run their analyses directly without waiting. They saved significant costs on duplicated storage and reduced 90 percent of dependency on the data engineering team. This freed the engineers to respond to ad hoc queries instantly instead of days, improving their workflow and overall productivity.