Zetaris is an analytical data virtualization platform that provides a no-code application programming interface for seamless integration of diverse data sources, enabling organizations to prepare their data for AI applications efficiently. The platform eliminates lengthy data preparation projects and manual logic building, allowing users to access high-quality, governed data quickly and cost-effectively.
Funding
$30M 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.
Founders
Product
Problem
Organizations struggle with integrating diverse data sources for AI applications due to lengthy data preparation projects and the need to manually build logic, resulting in delayed access to high-quality, governed data. Existing data infrastructure often creates barriers to connecting to data sources outside the vendor tech stack, leading to vendor lock-in and increased costs.
Solution
Zetaris offers an analytical data virtualization platform that provides a no-code application programming interface for seamless integration of diverse data sources, enabling organizations to efficiently prepare their data for AI applications. The platform removes barriers to connecting to data sources outside the vendor tech stack with a no-code data source integration. It removes the burden of long tail data preparation projects, giving you access to data, sooner. Zetaris removes the need to manually build business language and logic across data sets, with our no-code semantic layer and pre-baked industry models. The platform enables architectural optionality, empowering you to choose the right storage and tooling for the right price.
Target Audience
The primary audience includes data engineers, data scientists, and business/organizational leaders seeking to simplify and accelerate the building of valuable data products.
Features
- No-code data source integration for accessibility across vendor tech stacks
- Semantic layer and pre-baked industry models to remove the need to manually build business language and logic across data sets
- Architectural optionality to choose the right storage and tooling for the right price
- AI-powered data catalog with semantics and pre-identified relationships
- Unified semantic layer with Medallion/Datavault architecture
- Single pane of glass querying across lakehouse and enterprise data sources
- Connectors for databases, APIs, and BI tools
- Support for ETL/ELT workflows and big data platforms like Spark