Request-latency SLOs do not fit a pipeline of queues, functions and databases where users care about whether an order confirmation arrives in time. How we defined SLIs per hop, an end-to-end freshness SLO, how availability combines across steps, and how we alert on error budget burn.
Event-driven is not one pattern but four, and teams get hurt when they mix them up. We compare event notification, event-carried state transfer, event sourcing and CQRS on one billing example, with the costs each one adds and the signals that tell you which fits.
A refactor changed one output prefix in an image processing Lambda, and the function started triggering itself. Invocations went from 40,000 a day to 9 million in six hours. We stopped it with reserved concurrency, then separated input and output and added loop guards.
SQS, SNS and EventBridge all move messages on AWS, and teams often pick whichever they used last. We compare them on what actually differs: pull versus push, fan-out, filtering, ordering, retries and replay, then show the combinations we use in production.
Most trading bot failures we have seen are not strategy bugs; they are state bugs: lost fills, duplicate orders, wrong positions after a restart. This guide describes an architecture that keeps state correct: separated components, an order state machine, persistence, risk checks and reconciliation.
We put SNS in front of an SQS queue to fan out order events, and the consumer Lambda started failing with KeyError: 'orderId'. Each SQS message body was now an SNS envelope with our event as an escaped JSON string in Message. How raw message delivery fixes it, and what changes for filtering and attributes.