Get More From Your
Time-Series Data
Kinetica provides lightning-fast analytics on rapidly changing sensor, market, and streaming data at unparalleled scale. It merges the familiarity of a relational database with the speed of an accelerated analytics engine, designed for low-latency performance.
Advanced Time-Series Insights Made Easy
Kinetica behaves like a relational database you already knows and understand with powerful features for time series analysis
Real-time Window Analytics
Combine window functions with continuously updated materialized views to generate real time metrics on top of streaming time-series data. Window functions can be used to perform calculations on a subset of data over a fixed interval. We can use them to calculate time-series metrics like cumulative sums, ranks, and moving averages and more for precise temporal analysis.
Inexact AS-OF joins on timestamped data
Timestamp values from tables are rarely an exact match. Interval based functions like AS-OF help perform inexact joins to combine information from different time series tables that use timestamps. Kinetica can perform these inexact joins on streaming data making it possible to combine time stamped data from streaming inputs like IoT devices, stock market prices, and sensors.
Wrangle Time-Series Data with Ease
Kinetica provides a wide range of date and time, bucketing, trend functions and more. You can query with SQL or develop sophisticated applications using a REST API, or with language-specific libraries available for C++, C#, Java, Javascript, NodeJS & Python.
Engineered for Speed and Scalability
Kinetica is designed from the ground up to make working with large volumes of time-series data quicker and easier.
High Speed Ingest
Multi-head ingest and a lockless architecture allows you to keep up with large volumes of high-velocity data. This ensures the lowest possible latency from the time raw data is created until an answer can be returned to an ad-hoc query.
Fast Vectorized Query
Kinetica’s unique vectorized query engine can fully leverage the power of modern CPUs and GPUs to deliver exceptional performance – even with complex queries and joins on high cardinality data.
Scale-out Architecture
Kinetica’s distributed in-memory scale-out architecture is built for handling large and complex time-series use-cases. Tiered storage makes it possible to optimize where data lives for performance and cost.
Talk to Us!
The best way to appreciate the possibilities that Kinetica brings to high-performance real-time analytics is to see it in action.
Contact us, and we’ll give you a tour of Kinetica. We can also help you get started using it with your own data, your own schemas and your own queries.
