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Vessel data in real time with Apache Kafka instead of manual services.

For a vessel data analytics provider, we moved the existing architecture of Elasticsearch and manual services to a scalable streaming architecture with Apache Kafka.

  • Apache Kafka
  • Amazon MSK
  • Kafka Streams
  • Spring Boot
  • Java
  • Schema Registry
  • Kafka Connect
  • MirrorMaker
  • GitLab CI/CD
  • Kubernetes
  • DigitalOcean

01

The starting point

A provider of vessel data analytics processed raw vessel data with an architecture built on Elasticsearch and manual services. The goal was to move to a streaming architecture: raw data should be processed continuously and analysed for registered customers, and the system had to scale with growing data volumes.

02

Our approach

  1. 01 Selecting and provisioning a suitable Apache Kafka setup on Amazon MSK
  2. 02 Streaming architecture: raw data flows from an input topic into further partitioned topics, from which insights are extracted for registered customers
  3. 03 Implementing the microservices with Java, Spring Boot and Kafka Streams, including Schema Registry, Kafka Connect and MirrorMaker
  4. 04 Integration into the existing CI/CD environment with GitLab
  5. 05 Running the microservices on managed Kubernetes at DigitalOcean

03

The result

  • A streaming architecture instead of manual services
  • High scalability: because the vessel data is already partitioned, the services scale horizontally through replicas
  • Analyses for registered customers straight from the data streams
  • Automated delivery of the microservices via GitLab CI/CD

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