Practice : Real-time Event Streaming

Purpose and Strategic Importance

Real-time Event Streaming enables the capture, processing, and delivery of continuous data flows as they happen. By streaming events in real-time, teams gain immediate insights, automate reactions, and drive low-latency decision-making across platforms and products.

It powers modern use cases like fraud detection, IoT telemetry, user activity tracking, analytics pipelines, and real-time alerting - turning operational data into competitive advantage.

Description of the Practice

  • Systems publish events (e.g. transactions, state changes, user actions) to topics or streams.
  • Consumers subscribe to topics and process or react to these events in near real-time.
  • Popular platforms include Apache Kafka, AWS Kinesis, Google Pub/Sub, and Apache Pulsar.
  • Events are immutable, time-ordered, and often schema-validated for consistency.
  • Stream processing tools (e.g. Kafka Streams, Flink, Spark Streaming) enable filtering, aggregation, and enrichment.

How to Practise It (Playbook)

1. Getting Started

  • Identify a use case that would benefit from low-latency data (e.g. log aggregation, notification systems).
  • Select a streaming platform and define an initial topic and schema.
  • Create a producer service to emit events and a simple consumer to validate delivery.
  • Instrument producers and consumers with observability (e.g. lag, throughput, errors).

2. Scaling and Maturing

  • Partition topics for parallel processing and scalability.
  • Add schema validation and versioning using tools like Schema Registry.
  • Build fault-tolerant consumers with retries, dead-letter queues, and idempotency.
  • Develop stream processors to compute aggregates, joins, or real-time metrics.
  • Integrate with data warehouses, search engines, or dashboards for live analytics.

3. Team Behaviours to Encourage

  • Design events as shared contracts between systems and teams.
  • Treat events as product artefacts - documented, discoverable, and meaningful.
  • Monitor lag, throughput, and failure rates proactively.
  • Collaborate across producers and consumers to ensure schema stability and intent.

4. Watch Out For…

  • Schemas that change without coordination or versioning.
  • Under-provisioned infrastructure leading to dropped events or excessive lag.
  • Over-reliance on real-time without clear business need or SLAs.
  • Lack of observability making troubleshooting difficult.

5. Signals of Success

  • Data flows reliably and quickly from producers to consumers.
  • Event-driven use cases deliver measurable business outcomes.
  • Teams iterate rapidly on real-time features using stable schemas.
  • Event pipelines are observable, secure, and scalable.
  • Operational decisions are informed by live, trusted data streams.