The Kinetica Engine
In the Extreme Data Economy, data comes from unpredictable sources and analysis becomes much more complex.
Data can be big or small, static or streaming, structured or unstructured, human or machine. On top of that, it’s perishable: data’s useful life may only be hours or minutes, weeks or months or more. The siloed legacy databases built for serial computing that many businesses still have in place just can’t keep up.
CHALLENGES WITH EXISTING TECHNOLOGIES
Performance can’t keep up with extreme data: structured, unstructured, location, time, static or streaming.
Unwieldy Streaming Data
Batch processing leads to stale data and delayed decision making.
With lots of data technologies strung together, deployments become too complex.
Lacking AI Support
Minimal support for advanced algorithms, machine learning & deep analytics within the database.
The Kinetica Solution
The Kinetica engine intelligently combines GPUs, CPUs, and in-memory computing to analyze complex, unpredictable, streaming data with dramatically faster performance.
OLAP Query Engine
Perform real-time analytics on streaming or static data with up to 100 times the performance of legacy systems
Instantly generate and render visuals on billions of location points in milliseconds for faster dashboarding and discovery
Process location data as a native data type to perform advanced location analytics on massive datasets
Machine Learning Pipeline
Train models and run inference faster on the entire data corpus, not just small subsets with integrated ML workflows
and client apps
on-premises or cloud
Perform real-time analytics on streaming or static data with up to 100 times the performance of legacy systems.
Instantly generate and render visuals on billions of location points in milliseconds for faster analytical dashboarding and discovery.
Process location data as a native data type to perform advanced location analytics on massive datasets.
Train models and run inference faster on the entire data corpus, not just small subsets, with integrated machine learning workflows
Crunch large volumes of data fast with GPUs that work in parallel. Accelerate location-based and in-memory analytics, machine learning, and AI.
Abstracts the hardware and compute architectures to distribute workloads optimally across GPUs and CPUs.
Distributed Scale Out
Kinetica can scale up or scale out to support any dataset size. This leads to smaller and more predictable hardware costs.
Full text search functionality on the data itself, supported by Natural Language Processing (NLP).
Native Geospatial Capability
Make location a first class citizen. Run geospatial functions for filtering massive datasets by area, by track, custom shapes, and more exponentially faster than traditional systems.
Allow users to easily and intuitively slice and dice data and visualize datasets with Reveal, a built-in interactive dashboard.
Run a full range of SQL queries through our ODBC or JDBC connector on static or streaming data to process and analyze billions of rows in milliseconds.
Pre-built connectors for Apache Kafka, Apache NiFi, Apache Spark, Streamsets, and ODBC/JDBC make it simple to ingest data from multiple sources.
UDFs and Model Frameworks
Program with an extensible and highly flexible framework to perform advanced analytics and machine learning with data stored in Kinetica, taking full advantage of a distributed architecture.
The Kinetica engine is built for the enterprise,
with compression, high availability, and enhanced security
Optimize performance with a single insight engine with
a GPU database at its core.
Ingest and analyze streaming data from unpredictable sources in parallel,
in real-time, so you can capitalize on it now.
Analyze Streaming Data
Making it possible to analyze streaming and historical data together for the most accurate and more real time view of the world.
Ingest Data from
With new data types like location and time and with constantly emerging data sources, you need a single engine that can support it all.
Kinetica moves faster because we’re an insight engine powered by GPUs, running at the speed of memory with a built-in database, so you can take in and analyze data in parallel, in real-time.
Simplify Complex Analysis
Analyze data aggregated from hundreds of sources in real-time, combining complex algorithms and machine learning within the database without needing to integrate several different data management systems.