Real-time Analytics Database for Freshest Possible Insights
Lowest Data Latency
Distributed, Headless Ingest
Kinetica allows for scalable, parallel ingestion of large amounts of data, as the workload can be distributed across multiple machines and processes. This approach is used by Kinetica to efficiently ingest and process large volumes of data.
To avoid bottlenecks at the head node, ingestion can be organized in parallel by worker nodes. Headless ingest can be particularly beneficial in systems that need to support data ingestion from a large number of sources, as it allows data to be ingested in a decentralized and distributed manner, without the need for a central coordination point.
Native Kafka Connector
Kinetica’s Kafka connector makes it easy to stream data directly between Kafka and Kinetica and back again. The Kafka connector can be used to ingest high volumes of streaming data from a Kafka topic, or write data out from a Kinetica table to Kafka.
The Kinetica Kafka connector is one of a few that are Confluent Gold Certified, meaning it supports several advanced features including schema migration for the sink transfer, single message transform, and Control Center integration.
Kinetica's lockless architecture allows concurrent access to data without the use of table locks. This means that multiple users or processes (such as loading data) can access and modify the data simultaneously, without the need to obtain locks on the data or wait for other users to release locks. Most databases lock tables during the load process whereby users can still query the table but aren’t exposed to the new data being loaded until the load process is completed and the table is unlocked. Kinetica employs a lockless architecture that ensures data is available for query as soon as it lands.
Lowest Query Latency
Kinetica uses a fully vectorized query engine to boost performance. This is in contrast to conventional databases which process data on a row-by-row basis, which is usually much slower and requires more computational resources.
In a vectorized query engine, data is stored in fixed-size blocks called vectors, and query operations are performed on these vectors in parallel, rather than on individual data elements. This allows the query engine to process multiple data elements simultaneously, resulting in faster query execution and improved performance. In addition to improving query performance, this vectorized approach can also reduce the amount of compute and data engineering required, making them more efficient and cost-effective.
Continuously Updated Materialized Views
Kinetica's uses continuously updated materialized views to make data more accessible for high volumes of similar queries. Kinetica's use of vectorization allows these views to be calculated more often and more efficiently. Materialized views provide a pre-aggregated, read-optimized view of the data so that queries do less work when they run. Materialized views can be updated on query, on time-based refresh, or on change – producing a real-time high-throughput view of constantly changing data.
Performance at Scale
Kinetica's crushes other real-time databases in independent TPC-DS benchmarks. Most recently, Radiant Advisors compared Clickhouse with Kinetica using TPC-DS. Not only did Clickhouse fail to execute the vast majority of TPC-DS queries, but the ones it was able to execute revealed that Kinetica is 13x faster.
Try it yourself.
You can build out real-time applications quickly and easily with just SQL and a Kinetica Workbook. In this example, you'll enrich a streaming feed of market trades with historical market data and securities filings to build a view of how prices are changing over time. Set alerts when overall portfolio value changes beyond set amounts.
Common Operational Picture
Defense and public safety organizations use Kinetica to provide real-time interactive dashboards for insights on rapidly evolving situtations.
Cyber Threat Analysis
Watch how Kinetica can analyze over 2.5 billion rows of fast moving network data to understand and identify malicious threats at scale.
Making Sense of Sensor Data
As sensor data grows more complex, legacy data infrastructure struggles to keep pace. A new set of design patterns to unlock maximum value. Get this complimentary report from MIT Technology Review:
Book a Demo!
The best way to appreciate the possibilities that Kinetica brings to high-performance real-time analytics is to see it in action.
Contact us, and we'll give you a tour of Kinetica. We can also help you get started using it with your own data, your own schemas and your own queries.