for Time & Space
A vectorized analytics database – ideal for analyzing geospatial & temporal data at scale and speed
Vectorization Gives You Freedom
Real-Time AnalysisWith a lockless architecture and distributed ingestion, data is available immediately after it is written. No more waiting for data structures to be updated before new data can be found in queries.
Time & Space
Data level parallel processing unleashes significant performance improvements – particularly on spatial and temporal queries at scale. Aggregations, predicate joins, windowing functions, graph solvers all operate far more efficiently.
Parallel processing allows you to get away with simpler data structures. Spend less time engineering schemas, and watch as storage scales in proportion to the size of the data. This means less work, and lower hardware costs.
For Complex Geospatial Questions
Vectorized processing opens the door to highly performant geospatial analytics and detailed visualization of spatial-temporal datasets at speed and scale.
Work with with geospatial objects (points, shapes, tracks) and take advantage of over 130 vector-enhanced geospatial functions for filtering and joining data by area, by track, custom shapes and more. Spatial operations are exponentially faster for massive datasets than on traditional systems.
Geospatial Analytics »
Kinetica's OpenGL visualization pipeline is capable of drawing massive point datasets as WMS layers for use over maps. Kinetica can also generate heatmaps, scatterplots and even video. Native server-side rendering makes it possible to plot millions of points without bandwidth constraints.
Mapping & Visualization API »
Fuse Data across a Variety of Sources
Avoid complex data engineering with a single converged platform that is able to fuse together data across data lakes and streams to provide real time actionable intelligence.
Ingest from multiple sources
Pre-built connectors for Apache Kafka, Apache NiFi, Apache Spark, Spark Streaming, Apache Storm and others make it simple to ingest streaming data from a variety of sources and fuse it together.
See Examples »
Machine Learning Pipeline
UDFs offer an extensible and highly flexible framework for performing advanced analytics, predictive analytics and machine learning with data stored in Kinetica. UDFs have direct access to CUDA APIs, and can take full advantage of the distributed architecture of Kinetica.
In-Database Analytics »
REST & Native API's
API Documentation »
Time Series Data
Kinetica natively manages time-series data and window functions for efficient time series analysis. Powerful temporal selectors, with ASOF join filters so you can quickly gather data from the past.
Better Performance with Less Infrastructure.
The Kinetica engineering team obsesses over efficiency. With its vectorized architecture, dynamic tiered storage engine, and sophisticated compression, Kinetica requires far fewer resources than other comparable systems.
Large US Financial Institution700-node spark cluster running queries in hours took seconds on 16 nodes of Kinetica
Top US RetailerConsolidated 100 nodes of Cassandra(NoSQL) and Spark into 8 Kinetica nodes
Large PharmaIdentical performance between a 88-node Impala cluster and a 6-node Kinetica Intel cluster in Azure
On Premise or in the Cloud
Deploy Kinetica on your own hardware, bring your own license in the cloud, or the new native cloud solution on Azure.
- Available as a Docker Container
- SQL Analytics
- Spatial Functions
- Graph Server/Solvers
- WMS Visualization
- GAdmin Interface
- Reveal BI
The first cloud-native vectorized database across GPUs and CPUs.
- All features, plus:
- Available on Azure
- Choose GPU or CPU-only
- Flexible Pricing
- Fully Managed Service
- Quick Setup
- Kafka & Azure Blob Storage
- Kinetica Workbench (NEW)
Deploy on Premise, or bring your own license for cloud deployments
- All features, plus:
- GPU Enabled
- Cluster/Ring Resiliance HA
- Enterprise Support
- Active Analytics Workbench
- Cluster Computing
Built to Meet the Demands of the Modern Enterprise
Work with petabytes of data at speed with Kinetica's memory first, fully distributed architecture. Kinetica prioritizes and manages data across VRAM, RAM, disk, and cold storage and can create external tables for working with data in HDFS, S3 and Azure.
Kinetica provides secured access to data and data services by means of role-based access control. Permissions can be assigned at the table level, schema level, or globally, and can be assigned either directly or grouped into roles for assignment.
Eliminate single points of failure and recover gracefully. Kinetica offers node and process failover for in-cluster resiliency, and multiple clusters may be grouped in a ring resiliency to spread data and ensure eventual consistency.
Kinetica provides multi-faceted administration, installation, and configuration management tools with a centralized way to provision cloud hardware, configure cluster security, add/remove nodes, data backup and monitor cluster health.
Manage your Data, Explore your Data
Or, Book a Demo!
Sometimes marketing copy can sound too good to be true. The best way to appreciate the possibilities that Kinetica brings to large-scale geospatial analytics is to see it in action, or try it with your own data, your own schemas and your own queries.
Contact us, and we can set you up with a demo and a trial environment for you to experience it for yourself.