Honeydew provides a semantic layer for Snowflake that standardizes core business metrics like Active Users and Revenue, enabling teams to collaborate on definitions and automate technical tasks such as model building and metadata sharing. This centralized source of truth reduces analytics engineering efforts and ensures consistent, valid data usage across the organization.
Funding
$575K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
AG+1Founders
Product
Problem
Data teams struggle to maintain consistent business metrics across various analytics tools and reports, leading to discrepancies and hindering reliable decision-making. Defining and managing metrics like Active Users, Churn, and Revenue often requires significant analytics engineering effort and results in data silos.
Solution
Honeydew provides a semantic layer built natively for Snowflake, enabling data teams to standardize core business metrics and ensure consistent definitions across the organization. The platform facilitates collaboration on metric definitions and automates technical tasks such as model building, pre-aggregation, and metadata sharing. By centralizing business logic within the data warehouse, Honeydew eliminates data silos, optimizes costs, and empowers both data analysts and engineers to work more efficiently with a shared source of truth.
Target Audience
Honeydew is designed for data analysts, data engineers, and analytics leaders who use Snowflake and need a centralized, consistent, and reliable source of truth for business metrics.
Features
- Centralized metric store for defining and managing key business metrics
- Native integration with Snowflake for seamless data access and processing
- Automated model building and pre-aggregation to improve query performance
- Collaborative workspace for data teams to define and refine metric definitions
- Metadata sharing capabilities to ensure consistent data usage across tools
- SQL-based semantic layer for a shared language across people, tools, and data transformations