Vaultspeed provides a no-code, metadata-driven data modeling platform that automates data integration and business logic for cloud data warehouses, lakehouses, and meshes. This solution enables data teams to rapidly build and deploy data products in under two sprints, significantly reducing time-to-market and technical debt.
Funding
$20.3M 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
Data teams often struggle with the complexity and time-consuming nature of building and deploying data products in modern cloud data environments. Traditional data modeling approaches require extensive manual coding, leading to slow delivery times and increased technical debt. Integrating diverse data sources and implementing consistent business logic across different data domains further compounds these challenges.
Solution
Vaultspeed offers a no-code, metadata-driven data modeling platform that automates data integration and business logic for cloud data warehouses, lakehouses, and meshes. The platform enables data teams to rapidly build and deploy data products, significantly reducing time-to-market. By leveraging a metadata-driven approach, Vaultspeed creates a comprehensive relational data model that integrates diverse data sources based on business requirements and source metadata. The platform then automatically translates this metadata into data transformation code and workflows, supporting a wide range of technologies and setup combinations. Users can also create custom templates to automate business-specific logic, ensuring data quality and consistency across all data domains.
Target Audience
Vaultspeed targets data teams and data engineers working in organizations that are building data products in cloud data warehouses, lakehouses, or meshes.
Features
- No-code, metadata-driven data modeling environment
- Automated data integration with built-in templates for various technologies
- Custom template creation for automating business-specific logic and data quality rules
- Support for diverse data architectures, including data warehouses, data lakehouses, and data meshes
- Extensible automation template language for seamless integration with leading data platform solutions (Snowflake, Databricks, Microsoft, Google)
- Cloud-native setup for shared learnings and reduced total cost of ownership
- Metadata repository for versioning, delta releases, and migrations
- GUI for visual data model interaction and transformation