Preset offers a cloud-based analytics platform that aggregates and analyzes large datasets from diverse sources, enabling real-time insights for organizations. This platform enhances data-driven decision-making and collaboration among teams, addressing the challenge of fragmented data access and analysis.
Funding
$48.4M 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.
RFounders
Product
Problem
Organizations struggle with fragmented data access and analysis, hindering real-time insights and efficient data-driven decision-making. Existing business intelligence solutions can be complex, costly, and lead to vendor lock-in, limiting flexibility and control over data assets.
Solution
Preset provides a cloud-based business intelligence (BI) platform powered by the open-source Apache Superset project, offering a scalable and enterprise-grade hosted solution. The platform enables users to visualize and explore data through an intuitive drag-and-drop interface and fully interactive dashboards. SQL-savvy analysts can utilize the SQL IDE to run ad-hoc queries. Preset's dataset-centric approach allows users to create dashboards rapidly, freeing up data teams. The platform is designed to be agnostic to the underlying data architecture, eliminating the need for an additional ingestion layer.
Target Audience
Preset is designed for data teams and business users who need a cost-effective and flexible BI solution for data analysis, exploration, and visualization. It caters to organizations seeking to empower every team to be data-driven with interactive dashboards.
Features
- Fully-managed, cloud-hosted service for Apache Superset
- Managed Private Cloud option for enhanced security within a private cloud environment
- Embedded dashboards for integrating interactive analytics into custom applications
- REST API for managing Preset workspaces as code
- Intuitive drag-and-drop interface for easy dashboard creation
- SQL IDE for ad-hoc queries
- Dataset-centric approach for performant queries
- Agnostic data architecture, eliminating the need for an additional ingestion layer