Batch pipelines that run at midnight are not enough when the decision needs to happen in the next 60 seconds.

Streaming platform selection and setup based on your cloud environment, throughput, and operational capacity. Kafka or MSK for the widest connector and tooling support, Kinesis for a managed AWS-native path, Pulsar when tiered storage and native multi-tenancy matter. Topic design sizes partition counts to throughput and consumer parallelism, partition keys preserve per-entity event ordering, replication runs factor 3 across availability zones for durability, and everything is provisioned as infrastructure-as-code.

Stateful stream processing where logic aggregates across events, handles out-of-order arrivals, or maintains running computations over time windows. Event-time processing with watermarks places late-arriving events in the correct window and bounds how long a window waits before it closes, so a straggler event never stalls the whole aggregation. Tumbling, sliding, and session windows fit the shape of the question. Patterns include enrichment joins, windowed aggregations, and CEP sequence detection like three failed logins then a high-value transaction.

Change Data Capture reads the database transaction log rather than polling tables, capturing every insert, update, and delete with sub-millisecond latency and no query load on production. Each change publishes with before and after states and transaction metadata for exactly-once delivery, supporting real-time warehouse replication, audit logs, and event-driven microservices via the outbox pattern.

A stream is a contract between the team that produces events and every team that reads them. A schema registry enforces that contract at the broker, so a producer cannot ship an event that silently breaks a downstream consumer. We set compatibility rules (backward, forward, or full) to match how your teams deploy, version schemas explicitly, and design Avro or Protobuf payloads that add fields without a coordinated release across every consumer.

Real-time feature pipelines for ML models that need current values at inference time, like fraud models that need transaction counts from the last few minutes rather than last night's batch. Time-windowed aggregations write to the online store with sub-second latency, and the same feature logic runs in streaming and batch backfill so the model sees the same features at inference as in training.

Delivery from the streaming layer to multiple operational destinations, each consumer group tracking its own offset so a slow consumer never blocks the others. One event stream fans out to the fraud service, analytics pipeline, audit logger, and dashboards without coordination between consumers, and lag is monitored per destination with its own alert thresholds.

We pick the delivery guarantee your use case can afford. At-least-once is cheaper and fine when a consumer can deduplicate; exactly-once via Kafka transactions is worth the throughput cost when a duplicate charges a card twice or oversells stock. Monitoring covers the metrics that predict failures before they breach SLAs, not just the ones confirming they already have. Events that fail after retries land in a dead letter queue with the failure reason and are replayable after a fix, and backpressure pauses polling so lag accumulates in durable retention rather than in memory.