HyperQuery provides a unified data platform for connecting and querying disparate business systems. It enables users to build complex SQL queries across multiple SaaS applications without manual data extraction or warehousing. This service streamlines business intelligence and operational reporting by centralizing access to siloed application data.
Funding
Funding not disclosed
Founders
Product
Problem
Data teams often struggle with fragmented workflows, relying on a mix of tools like Jupyter notebooks, SQL IDEs, and BI platforms, leading to disorganized analyses and difficulties in knowledge sharing. This scattered approach hinders collaboration and makes it challenging to maintain a single source of truth for data insights.
Solution
Hyperquery is a collaborative data notebook that unifies SQL, Python, spreadsheet functionality, and visualizations into a single environment, streamlining data analysis workflows. It enables data teams to create interactive analyses, dashboards, and self-service data applications within organized project spaces, fostering efficient knowledge sharing and collaboration. By integrating with existing data stacks and business tools, Hyperquery allows users to connect to data warehouses, data models, and embed documents into platforms like Notion and Confluence. The platform offers features such as seamless auto-complete for SQL queries, the ability to load data from queries into dataframes for advanced manipulation, and tools to create advanced visualizations.
Target Audience
Hyperquery targets data scientists, data analysts, and other data professionals who need a collaborative environment for data exploration, analysis, and sharing of insights.
Features
- Collaborative data notebook for code, charts, and text
- Seamless integration of SQL, Python, and spreadsheet functionalities
- SQL editor with auto-complete, Jinja templating, and parameterization
- Python integration for advanced data manipulation and statistical modeling
- Ability to create interactive analyses, dashboards, and self-service data applications
- Organized project spaces for efficient knowledge sharing
- Integration with data warehouses like Snowflake, BigQuery, and Redshift
- Embeddable documents and blocks for integration with business systems like Notion and Confluence
- SOC 2 Type II, HIPAA, and GDPR compliance for enterprise-grade security