𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗥𝗲𝗮𝗹 𝗧𝗶𝗺𝗲 𝗗𝗮𝘁𝗮 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀
Real-time analytics is hard. Teams often fight broken pipelines and hidden failures. You need a system built for observability.
Start with your goals. Define these metrics first:
- Latency: How fresh is the data?
- Throughput: How many events move per second?
- Accuracy: Is the data correct?
Build your architecture in layers. Keep them separate to scale them alone.
- Ingestion: Use Kafka or Kinesis.
- Processing: Use Flink or Spark.
- Storage: Use ClickHouse or S3.
- Serving: Use APIs or dashboards.
Use a schema registry. This prevents breaking changes. Define event types with clear keys and timestamps. Store both event time and process time.
Observability is your backbone. Use these three pillars:
- Metrics: Track lag and error rates.
- Traces: Use IDs to follow data across services.
- Logs: Use structured logs with context.
Make your system resilient.
- Use dead-letter queues for bad events.
- Make operations idempotent to stop duplicates.
- Roll out changes with canary deployments.
Start with a lean stack. Use Kafka, Flink, and ClickHouse. Add OpenTelemetry for visibility.