Kinetica is a database, purpose built for real-time analytics at scale.
Kinetica leverages vectorized memory-first architecture with kernels that have
been custom built for over a decade to deliver blistering performance at scale
on significantly less infrastructure than traditional data warehouses. Using a
highly-distributed, lockless design, Kinetica enables simultaneous ingestion
and analysis with integrated geospatial, graph, SQL, and AI/ML capabilities.
With out of the box connectors for ingest and egress, native language bindings
and a rich API ecosystem, developers can leverage the tools that they are
comfortable and familiar with to build and deploy advanced analytical
applications. With the rapid emergence of IoT applications and the exponential
explosion of data volumes, architecture teams across every industry are
searching for a more cost effective and performant means to manage and analyze
that data - we suggest you start with Kinetica!
From a user’s perspective, data in Kinetica is organized in a manner similar
to a standard relational database management system (RDBMS). A Kinetica
database consists of tables, each contained by a schema. The available
column types include the standard base types (int,
long, float, double, string, & bytes), as well as numerous sub-types
supporting date/time, geospatial, and other data forms. The native API
interface to the system is that of an object-based datastore, with each object
corresponding to a row in a table.
Kinetica provides basic functionality to create tables, add rows, read rows,
and delete rows. What really separates Kinetica is its specialized filtering
and visualization functions. These functions can be performed through our
native API or our ODBC/JDBC connectors, which are SQL-92, SQL-99, &
SQL:2003 compatible. This allows users to integrate Kinetica with
third-party GUIs and developers to quickly integrate existing code with
Kinetica.
Kinetica has a distributed architecture that has been designed for data
processing at scale. A standard cluster consists of identical nodes run on
commodity hardware. A single node is chosen to be the
head aggregation node.
Kinetica is designed to be highly scalable. A cluster can be scaled up at any
time to increase storage capacity and processing power, with near-linear scale
processing improvements for most operations. Sharding of data can be done
automatically, or specified and optimized by the user.
Kinetica is an ODBC-compatible database, supporting ANSI SQL-92 compliant
syntax. Further, its native API can be accessed via RESTful HTTP endpoints using
either JSON or Avro Serialization methods. Officially supported and open-source
language bindings are provided for Java, Python, JavaScript, C++, and
C#. Additional language bindings can be constructed for any language capable
of HTTP requests and JSON parsing.Kinetica also connects seamlessly to a variety of other data processing and
analytical frameworks, including Apache Spark, Storm, and NiFi.
Host management services allow the cluster to be brought up & down and to have
its status monitored from a single node. Cluster management utilities allow all
nodes to be upgraded, modified, & maintained from one location.
Assistant
Responses are generated using AI and may contain mistakes.
