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Massively Parallel Distributed Architecture

Kinetica’s modern analytics architecture enables unified analytics at unprecedented performance and scale. This is powered by a distributed, memory first, vectorized database that was built from the ground up to take advantage of modern CPU and GPU architectures. Analyze petabytes of data, take advantage of horizontal scalability, and optimize TCO with tiered storage.


Scale-Out Analytics for the Performance You Need

At the core of Kinetica is a distributed, columnar, memory-first database designed for analytical (OLAP) workloads that runs on GPUs and CPUs. Data is stored across multiple nodes for parallel processing, with most frequently used data pre-loaded into RAM to eliminate IO bottlenecks.

Demo: GPU-Accelerated Object Detection with Kinetica, Oracle Cloud, and the SF Estuary Institute

Utilize Modern Compute Platforms with GPU-Acceleration

Purpose-built to leverage extreme parallel computing power, such as GPUs and multi-core CPUs, Kinetica optimally routes query processing in each node across CPUs and GPUs for fastest results. Use industry standard SQL to query and analyze billions of rows of data in a matter of microseconds.

Customer Story: GSK uses Kinetica to Develop New Medicines Faster

Combine Analytical Techniques

Kinetica blends a full range of analytical processing including relational, text search, time series analysis, location intelligence, graph analytics, streaming analytics and machine learning in a single platform.

Run at “Big Data” Scale

Designed for enterprise scale, Kinetica can operate on your entire data corpus by intelligently managing data across GPU memory, system memory, disk / SSD, HDFS, and cloud storage like S3 for optimal performance. Kinetica can also query and process data stored in data lakes, joining it with data managed by Kinetica in highly parallelized queries.

Tech Talk: Tiering Kinetica to “Data Lake” Scale

Real-Time Streaming, Location, and Graph at Scale for Smart City Analysis

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