Client

Rate limiting is built in: configure token buckets, sliding windows, or adaptive limits that respond to backpressure.

Metrics are published to standard formats (Prometheus, CloudWatch, DataDog) with zero configuration.

await stream.on('user.created', async (event) =>  console.log(event.data.email); );

Error handling is explicit: catch failures at any stage, log them, and route bad events to a dead-letter queue without stopping the main pipeline.

Client

Authentication tokens are rotated silently in the background; your code never handles credentials directly.

await stream.ack(event.id); const pipeline = stream.filter(e => e.amount > 100).map(e => ( ...e, fee: e.amount * 0.03 )).on('error', (err) => logger.error(err));

Batching is automatic—control it via `batch(100)` or `batch({ size: 50, timeoutMs: 1000 })` to trade latency for throughput.