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Datawatch Adds Spark, Kafka Support With Panopticon

Update is designed to bring faster analytics to the trading, risk and compliance markets.

Written By

May 18, 2016

Datawatch has added Apache Spark and Kafka support to its data visualization software Panopticon 13.7, as well as deployment via HTML 5.

“Our new support for Spark and Kafka, along with enhanced and new visualizations, gives customers the ability to quickly identify, investigate and act on anomalies in ever-increasing volumes of data,” said Peter Simpson, vice president of visualization strategy at Datawatch, in an announcement.

The Panopticon software is targeted at financial markets and similar environments, allowing firms and exchanges to perform real-time, intra-day and historical analysis of electronic trading.

There are two components: Panopticon Designer and Panopticon Server. The designer component allows enterprise users to quickly create visual apps on their desktop, and the server allows for deployment via HTML5.

Datawatch said the update also meets customer requirements and regulatory compliance across all asset classes as well as the ability to support big data architectures. It is designed to seamlessly integrate into e-trading environments to detect and instantly alert of any anomalies.

According to their release, key features include:

Real-time streaming updates -with the addition of web socket and Apache Kafka connectivity
Big data connectivity – includes popular platforms such as Spark SQL, Kafka, Cloudera Impala, Cassandra and more.
Visual analysis– improved visualizations, cross tabbing, axis scaling and outlier screening.
Tick data connectivity- Improved connectivity with OneMarketData OneTick and Kx Systems kdb+

Other features include analytical dashboard design and publishing and secure access through any web browser. Users can easily access both real-time and historical analysis and insights in one place. Historical data can be played back over time frames or by individual transactions.

Data can have an automatic refresh period set from one second to one day. A variety of data sources are available including Tick Databases, complex event processing engines, message buses, web, Cube, and big data platforms.