Principles and Patterns for Streaming Data Analysis
summary
Architecture shows concrete components and outcomes: collected two hours of Swiss transport streams (station boards and train positions), pushed into an Infinispan 3-node cluster with replication factor 2 running on OpenShift / Kubernetes, analyzed with Vert.x + Rx-style flowables and continuous queries that publish delayed-train events to an event bus, then delivered over WebSockets to a JavaFX dashboard and a Google Maps view. Demo proves nonblocking scaling, discusses delivery semantics (at-least-once vs “exactly once”), and runs on a laptop.
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chapters (19)
- 0:00Introduction
- 1:25Data is overwhelming
- 3:04Interaction patterns
- 7:00Buffers
- 8:31Guarantees
- 9:28Exactly once
- 11:05Initial Collection Tier
- 11:20Analysis Tier
- 12:02Continuous Queries
- 13:20Time Constraints
- 13:51Other Topics
- 14:50Data Access Theory
- 17:26Openshift
- 17:57Kubernetes
- 18:38Vertex
- 18:54Erics Java
- 20:26FinishPlan
- 23:50Demo
- 36:00Google Maps
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