Bringing real-time analytics on temporal and vector data to Snowflake
Want to run production-ready, time-series analytics and vector-processing workloads without changing code or leaving the Snowflake environment? Our partnership with Snowflake lets you perform fast, scalable, temporal, and vector data analytics for time series and AI workloads on the Snowflake Data Cloud.
Accelerate time-series workloads
Run complex analytics on high volumes of historical, time series data quickly, efficiently and at scale.
Faster insights
Close the ‘time-to-insight’ gap between the edge and the data, compressing product life cycles and the development of real-time MLOps pipelines.
Rapid deployment
Build and deploy trading analytics solutions and innovative enterprise-class time series analytics applications.
Data management
Enhance performance for queries, real-time computations, and similarity searches on Snowflake data without moving or duplicating it.
Why KX for Snowflake?
PyKX + Snowpark
Data engineers and data scientists can run time series analytics using Python without leaving the Snowflake Cloud.
No more siloed data teams
Extend access and collaboration across teams, workloads, clouds, and data, seamlessly and securely.
Enhance quant capabilities
The combination of powerful temporal and vector data analytics with data sharing is ideal for use cases like risk management, forecasting, and predictive analytics.
Security & governance
Protect, store, and access all portfolio, reference, market, and risk data with strict governance controls.
