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All at Sea with Streams - Using Kafka to Detect Patterns in the Behaviour of Ships

The great thing about streams of real-time events is that they can be used to spot behaviours as they happen and respond to them as needed. Instead of waiting until tomorrow to find out what happened yesterday, we can act on things straight away.

This talk will show a real-life example of one particular pattern that it's useful to detect—ships engaged in potentially suspicious behaviour at sea. Transhipping is often used for legitimate purposes to optimise efficiencies but can also be used for nefarious purposes such as illegal fishing.

By capturing streams of maritime AIS data in real-time into Kafka and processing it with ksqlDB, it's possible to detect the kind of characteristics that could indicate behaviour of interest, such as ships moving slowly at close proximity for a length of time.

I'll demonstrate how the data was ingested from a raw TCP feed, unified with reference data from CSV files, and then processed to spot patterns with the resulting real-time stream of matches written to a new Kafka topic for validation and analysis.


Robin Moffatt

Robin is a Developer Advocate at Confluent, the company founded by the original creators of Apache Kafka, as well as an Oracle Groundbreaker Ambassador. His career has always involved data, from the old worlds of COBOL and DB2, through the worlds of Oracle and Hadoop, and into the current world with Kafka. His particular interests are analytics, systems architecture, performance testing and optimization. He blogs at http://cnfl.io/rmoff and http://rmoff.net/ and can be found tweeting grumpy geek thoughts as @rmoff. Outside of work he enjoys drinking good beer and eating fried breakfasts, although generally not at the same time.