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Real-time Database for Analytics & Decisioning

Kinetica enables you to quickly build real-time, event-driven analytics systems with ease.

Combine live streaming data with stateful relational data, all in one database, and without the need to tie together a collection of piecemeal technologies.

Combine Stream Processing with Relational Insight

Bringing context to streaming data has always been difficult.  Kinetica goes beyond stream processing tools to provide a simple, unified data platform for streaming data analytics at scale.

Other data systems all have critical flaws when it comes to real-time analysis on moving data – most aren't designed to handle the high-cardinality joins and the constant aggregation and re-aggregation as new data changes the picture. Stream processors lack sophistication. Batch processing in data warehouses is too slow and only gives a rear-view picture. Assembling a variety of specialized technologies to build real-time systems soon becomes complex

Stream processors lack sophistication, batch processing in data warehouses is too slow, and assembling a variety of piecemeal technologies to build real-time systems becomes complex fast.
Radiant Advisors - Spatial and Time-Series Databases
"Kinetica outperformed PostGIS in every query and was the only database to pass all feasibility tests across geospatial, time-series, graph, and streaming."

Example: A Bike Sharing Tracker

Real-time applications can be built quickly and easily with just SQL in a Kinetica Workbook (or any other SQL editor).

Take this example: a bike sharing company wants to track the status of bikes out for rent and generate notifications when a docking station is low on bikes.

You can combine a kafka feed from bike docking stations, with static information on the bike stations that might even be held externally. This can be brought together to create a live chart of inventory, and generate alerts when inventory falls below a set level.


    LOCATION = '',
        'Kafka_topic_name' = 'station status',
        credential = 'MY_CONFLUENT_CRED')

LOAD DATA INTO station_status
  DATA SOURCE = 'bk',
  'type_inference_mode' = 'SPEED')

Ingest Streaming Data from a Variety of Sources

Kinetica can ingest data at high speed from a variety sources. Ingestion can be distributed over multiple nodes. Kinetica's Kafka Connector makes it simple to attach to high-volume streaming feeds.

Aggregate, Fuse, and Analyze

Streaming data can be easily be fused with stateful data for context or aggregations.

AS SELECT DECIMAL(REGR_INTERCEPT(s1.total_capacity - s2.num_available), s2.last_reported)) as b, DECIMAL(REGR_SLOPE(s1.total_capacity - s2.num_available), s2.last_reported) as a, s1.station_id
FROM station_information s1, station_status_historical s2
WHERE s1.station_id = s2.station_id
GROUP BY station_id

AS  SELECT  ROUND((r.b + (r.a * DECIMAL(s2.last_reported)))) as bike_demand, s2.station_id, s2.num_available  
FROM  streaming_station_status s2, regression_view r where s2.station_id = r.station_id

Streaming Materialized Views

Create continuously updated views - or graphs, models, or just make data available for query. 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.

Here we create a new view to show the running demand for bikes at a station.



Event Triggers Based on Decisioning Rules

Create alerts on moving data. It's super simple to build sophisticated event-driven pipelines using just SQL straight from Kinetica


CREATE STREAM demand_alert ON TABLE streaming_demand
WHERE bike_demand > num_available
WITH OPTIONS (event = 'insert' , 
datasink_name =  | 
increasing_column = 'last_reported'))
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More Examples...

See how Kinetica is being used to power low-latency, real-time applications.

Real-Time Risk Analysis

See how Kinetica is being used in financial services to provide a continuously running picture of exposure and risk,

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.

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.