aws-messaging-and-streaming

name	aws-messaging-and-streaming
description	Guides use of AWS messaging and streaming services. Covers Amazon SQS, Amazon SNS, Amazon EventBridge, Amazon MQ, Amazon Kinesis Data Streams, Amazon Data Firehose, Amazon Managed Service for Apache Flink, and Amazon Managed Streaming for Apache Kafka (MSK). Use when implementing messaging and streaming patterns.
version	1
metadata	{"service":["sqs","sns","eventbridge","mq","kinesis","kds","firehose","flink","msf","msk","kafka"],"task":["learn","architect","debug","deploy"],"persona":["developer","architect","devops"],"workload":["serverless","event-driven","streaming","data-analytics"]}

AWS Messaging & Streaming Services

When answering AWS messaging and streaming questions, verify specific numbers, versions, limits, and behavioral details from service-specific skills or official AWS documentation. When uncertain, search skills or docs rather than guessing. Fabricated configuration options or incorrect version numbers are worse than admitting uncertainty.

When a question asks about recommended configurations (CloudWatch alarm settings, thresholds, missing data treatment), search for the service-specific skills or documentation rather than relying on general best practices.

Overview

Domain expertise for choosing and using AWS services that move data between producers and consumers. This skill covers two fundamental patterns — messaging and streaming — and the AWS services that implement each. Use this skill to decide which pattern fits a workload, select the right service, and understand how services integrate with each other.

For specific guidance on individual AWS services, see reference files or service-specific Skills.

Streaming and Messaging

What Is Messaging?

Messaging enables decoupled, asynchronous communication between components. A producer sends a message; one or more consumers receive and process it. Once processed, the message is typically deleted. Messaging services handle delivery guarantees, retries, and dead-letter routing.

Key characteristics:

• Messages are consumed once (point-to-point) or fanned out (pub/sub), then removed
• No replay — once acknowledged, a message is gone
• Designed for command/request workloads, task distribution, and event notification

What Is Streaming?

Streaming enables ordered, durable, high-throughput continuous data flow. Producers append records to a log; consumers read from positions in that log. Records persist for a configurable retention period regardless of consumption.

Key characteristics:

• Records are retained and replayable within the retention window
• Strict ordering within a partition/shard
• Multiple independent consumers can read the same data at different positions
• Designed for event sourcing, real-time analytics, change data capture, and continuous processing

Key Differences

Dimension	Messaging	Streaming
Data lifecycle	Deleted after consumption	Retained for replay (hours to indefinitely)
Ordering	Best-effort (Standard) or per-group (FIFO)	Strict per-partition/shard
Consumer model	Competing consumers (work distribution)	Independent readers (fan-out by position)
Throughput pattern	Bursty, variable	Sustained, high-volume
Replay	Not supported (except DLQ redrive)	Native — seek to any position in retention
Typical latency	Milliseconds (push or short-poll)	Milliseconds to low seconds
Scaling unit	Concurrency (consumers/pollers)	Partitions or shards

Messaging Use Cases

• Decoupling microservices with request/response or command patterns
• Distributing work across a pool of competing consumers (task queues)
• Fan-out notifications where each subscriber acts independently
• Workloads that are bursty and benefit from queue buffering
• Migrating existing JMS/AMQP applications (Amazon MQ)

Streaming Use Cases

• Continuous, high-throughput data ingestion (logs, metrics, clickstreams, IoT telemetry)
• Event sourcing where consumers need to replay from any point in time
• Multiple independent consumers processing the same data differently
• Real-time analytics, windowed aggregations, or complex event processing
• Change data capture (CDC) pipelines

Messaging Services

These services are generally used for messaging workloads. Sometimes streaming services (Kinesis Data Streams, Managed Streaming for Apache Kafka) are also used for messaging workloads, depending on exact use case and requirements.

Service	Best For	Key Differentiator
Amazon SQS	Task queues, decoupling, buffering	Fully managed, unlimited throughput (Standard), exactly-once (FIFO), fair queues for multi-tenant workloads
Amazon SNS	Fan-out, pub/sub notifications	Push to multiple subscribers (SQS, Lambda, HTTP, email, SMS)
Amazon EventBridge	Event routing, cross-account/SaaS integration	Content-based filtering, schema registry, 200+ AWS source integrations
Amazon MQ	Lift-and-shift of existing JMS/AMQP/MQTT apps	Protocol compatibility (ActiveMQ, RabbitMQ) for legacy migration

Streaming Services

These services are generally used for streaming workloads.

Service	Best For	Key Differentiator
Amazon Kinesis Data Streams	Real-time ingestion with AWS-native consumers	On-demand Advantage mode (instant scaling, no shard management), 1–365 day retention
Amazon Data Firehose	Zero-admin delivery to storage/analytics	Auto-scales, buffers, batches, and delivers to destinations
Amazon Managed Service for Apache Flink	Complex stream processing (joins, windows, state)	Full Apache Flink runtime — SQL, Java, Python APIs for stateful computation
Amazon MSK	Kafka-native workloads, ecosystem compatibility	Apache Kafka API, Express brokers (3x throughput, 20x faster scaling compared to Standard brokers), broad connector ecosystem

Common Integration Gotchas

• SQS system vs. user message attributes: Attributes like AWSTraceHeader (set by X-Ray / EventBridge / Pipes when sending to an SQS DLQ) and SenderId, SentTimestamp are SQS system attributes, NOT user message attributes. They are never returned by default from ReceiveMessage — request them explicitly via AttributeNames=[...] (or MessageSystemAttributeNames), separate from MessageAttributeNames which fetches user attributes. This matters for DLQs, where the trace header rides on the system attribute and the user-attributes slot carries the service's failure metadata (e.g. EventBridge's RULE_ARN, ERROR_CODE).

• SNS → Firehose → S3 record separator: For SNS subscriptions using the firehose protocol that land in S3, records are already newline-delimited by default (NDJSON). Do NOT turn on Firehose's AppendDelimiterToRecord — SNS emits the newline itself, and enabling the processor produces double newlines.

• EventBridge rule target DLQ + SNS subscription DLQ both need a DLQ queue policy. Attaching the DLQ alone is not enough — the DLQ silently drops messages until its queue policy allows the service principal. EventBridge: PutTargets with DeadLetterConfig.Arn=<DLQ>, plus SQS policy Allow sqs:SendMessage for Service: events.amazonaws.com with aws:SourceArn = the rule ARN. SNS: SetSubscriptionAttributes RedrivePolicy={"deadLetterTargetArn":"<DLQ>"}, plus SQS policy allowing Service: sns.amazonaws.com scoped by the topic ARN.

• SQS production defaults: long polling + customer-managed encryption. New queues default to short-poll (ReceiveMessageWaitTimeSeconds=0) and SSE-SQS (AWS-owned key). For production, SetQueueAttributes with ReceiveMessageWaitTimeSeconds=20 (long polling) and KmsMasterKeyId=<customer-managed key id/ARN> rather than leaving alias/aws/sqs.

• Broker and Kafka credentials belong in Secrets Manager, not connection strings. Do not hardcode usernames, passwords, or SASL/SCRAM credentials in application config, env vars, JAAS files, or IaC. For Amazon MQ (ActiveMQ/RabbitMQ) store broker users as secrets and fetch at startup; Lambda event source mappings for Amazon MQ require the broker credentials to be supplied as a Secrets Manager secret ARN (BASIC_AUTH), not inline. For MSK SASL/SCRAM the secret is not optional: it must be named with the AmazonMSK_ prefix and encrypted with a customer-managed KMS key (secrets created with the default aws/secretsmanager key cannot be associated with a cluster), then attached via BatchAssociateScramSecret. Lambda event source mappings for MSK (SASL/SCRAM or mTLS) and self-managed Kafka also reference a Secrets Manager secret ARN rather than inline credentials. Enable rotation and scope IAM read access (secretsmanager:GetSecretValue) to the consuming role only. See AWS Well-Architected SEC02-BP03 Store and use secrets securely.

• Service-principal resource policies need aws:SourceArn / aws:SourceAccount conditions. When a queue or topic policy grants a service principal like events.amazonaws.com, sns.amazonaws.com, or s3.amazonaws.com permission to sqs:SendMessage or sns:Publish, omitting source conditions opens a confused-deputy hole — any rule, topic, or bucket in any AWS account can drive writes. Scope every such statement with aws:SourceArn (the specific rule/topic/bucket/pipe ARN; use ArnLike with * when the ARN isn't fully known yet) and aws:SourceAccount (your account ID). For S3 event notifications both keys are required because S3 bucket ARNs don't carry the account ID, so aws:SourceArn alone doesn't constrain the account. The same pattern applies to role trust policies for IAM roles used by EventBridge rules and EventBridge Pipes (principal events.amazonaws.com / pipes.amazonaws.com, aws:SourceArn = the rule or pipe ARN) — not just the DLQ case called out above. See the IAM User Guide on The confused deputy problem.