Kinetica v6.1 is Now Available
We’re excited to announce the release of Kinetica, version 6.1 – our second major release of the year.
Among the many improvements are enhanced geospatial processing, GPU-accelerated visualizations, added SQL capabilities, a re-worked administration experience and greatly simplified deployment. This release will give our customers more capabilities for analytics at speed and at scale.
Let’s take a closer look at some of these improvements:
Accelerated Geospatial Visualizations
Kinetica has moved to a new OpenGL based rendering pipeline to power our visualizations, resulting in massive performance benefits when working with complex geometries and visualizations. In particular, polygon and line geometries with high vertex counts, scatter plots and complex graphs or charts will likely see visualizations generated in orders of hundreds of magnitudes faster.
Enhanced Geospatial Processing Support
This release introduces over 80 functions and filters for geospatial users, including spatial joins, clipping, geometry splits, difference calculations and much more.
The new geometry functions are now available through SQL, in addition to the REST interface, making it quicker and easier for business analysts to do spatial analytics at scale on large datasets.
For example, in retail where geometries might be defined to outline territories for a given store, it would be possible to look at customer behavior to spot areas where customers are driving out of their way to visit a different store. Such anomalies might highlight issues that might need to be addressed. Kinetica is able to deliver results to such queries in real time, and on large datasets with complex geometries.
Take a look at the full list of the new geospatial SQL functions and geospatial query examples with Python API
Never before has it been possible to perform sophisticated spatial analysis at the speed and scale that Kinetica makes possible.
New Administration Tools
With this latest release, administration of Kinetica has become much, much easier. Our new admin interface makes it a breeze to import, manage, and work with your data. The tool includes cluster management features for monitoring startup, managing processes and nodes and optimizing cluster-wide performance. Table management features make it possible to easily work with data, apply compression, encoding, and security.
When you’re trying to support production data, ease of administration makes all the difference. Kinetica makes it possible to land big datasets quickly, and efficiently work with them, even at scale.
Maturity for OLAP Workloads and Enterprise Usage
Speed at scale is what makes Kinetica unique. With the latest version, Kinetica continues to mature for OLAP type workloads:
Data can now be compressed by column for base data tables, or use dictionary encoding by column for low cardinality enumerations.
SQL support continues to improve with the ability to INSERT, UPDATE, DELETE, CREATE TABLE and ALTER tables. A new DATETIME format allows for string input and output of timestamps.
Security is now enhanced with mapping of roles to LDAP/Active Directory groups, and improved auditing capability per user per endpoint.
Simplified Deployment
Kinetica is now easier than ever to get started with. Deployment can be managed from the user-interface with no config file or complex command-line interactions required. Typical setup time, even for a multi-node deployment, should be as little as 10 minutes. And you can try this yourself. Sign up now for your invite to try Kinetica at https://www.kinetica.com/trial/.
Deploy in the Cloud
Kinetica will also be available on Azure and AWS one-click by October 31. New automated deployment options remove risk and complexity, and make it simpler for enterprises to quickly get started with an accelerated compute database in the cloud.
The latest release of Kinetica’s in-memory, GPU-accelerated compute database is immediately available for existing customers. For a consultation with one of our sales engineers or to get a demo of Kinetica, contact us here.