AWS Messaging And Streaming
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides architectural guidance for choosing between messaging and streaming patterns within AWS. It helps users distinguish between application-to-application data movement (using services like SQS, SNS, EventBridge, and Kinesis) and application-to-person communication. It includes best practices for service integration, security, and common configuration pitfalls, while providing a clear routing mechanism to specialized skills for customer-facing communication channels.
What it does
- Explains the core differences between messaging and streaming patterns.
- Maps specific AWS services to messaging or streaming use cases.
- Provides architectural guidance on when to use SQS, SNS, EventBridge, MQ, Kinesis, or Firehose.
- Identifies the correct AWS service for customer communication channels like email, SMS, and push notifications.
- Routes users to specialized skills for detailed service configuration or troubleshooting.
- Highlights common integration gotchas regarding resource policies, DLQs, and secret management.
When to use it
- When deciding whether a workload requires a messaging queue or a streaming data pipeline.
- When you need to determine which AWS service is appropriate for a specific communication channel.
- When troubleshooting common integration issues like SQS attribute access or SNS-to-Firehose record formatting.
- When you need to understand the security requirements for connecting to managed brokers or streaming clusters.
Advantages
- Clarifies the boundary between application-to-application and application-to-person communication.
- Prevents common security misconfigurations like the confused deputy problem.
- Provides a structured framework for selecting the right AWS messaging or streaming service.
- Reduces configuration errors by highlighting specific integration nuances for AWS services.
Usage examples
- “Should I use SQS or Kinesis for my microservices communication?”
- The agent will compare the two based on your requirements for ordering, replayability, and throughput, helping you choose between a task-queue model and a streaming-log model.
- “How do I send a notification to a customer's phone?”
- The agent will identify the appropriate AWS End User Messaging service and direct you to the specific skill or documentation for that channel.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/aws/aws-messaging-and-streaming/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip aws-messaging-and-streaming.zip -d .agents/skills
Documentation by AWS
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. It also marks the boundary with customer communication — messages delivered to people rather than to application components — and routes those questions to the skill or AWS documentation that owns each channel (see Customer Communications (Application-to-Person)). 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 endpoints suit operational alerts — for customer email or SMS see Customer Communications (Application-to-Person)) |
| 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 |
Customer Communications (Application-to-Person)
The services above move data application-to-application, between components of the same application. A separate group of AWS services is application-to-person (A2P): it delivers messages to — or receives them from — recipients outside the application, such as customers and subscribers. The two groups are not interchangeable.
| Channel | Service | Skill |
|---|---|---|
| Amazon SES | amazon-ses | |
| AWS End User Messaging Social | aws-social-messaging | |
| SMS, MMS, RCS, voice | AWS End User Messaging SMS | aws-sms-voice |
| Mobile push | AWS End User Messaging Push | None |
Answer two kinds of question directly from this section: which group a workload belongs to, and which service owns a channel.
For every other customer communication question — setting up a channel, sending through it, or troubleshooting delivery — do not answer from this skill: load the skill named in the table and answer from that.
To load it, use aws___retrieve_skill(skill_name="<skill>") with the exact name from the table when the AWS MCP server is available, or read the skill document from the Agent Toolkit at skills/<skill>/SKILL.md.
Where the table says None, or the named skill cannot be loaded, say so, then answer using the documentation tools (aws___search_documentation, aws___read_documentation) if available, or the AWS documentation for the named service otherwise.
Common Integration Gotchas
SQS system vs. user message attributes: Attributes like
AWSTraceHeader(set by X-Ray / EventBridge / Pipes when sending to an SQS DLQ) andSenderId,SentTimestampare SQS system attributes, NOT user message attributes. They are never returned by default fromReceiveMessage— request them explicitly viaAttributeNames=[...](orMessageSystemAttributeNames), separate fromMessageAttributeNameswhich 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'sRULE_ARN,ERROR_CODE).SNS → Firehose → S3 record separator: For SNS subscriptions using the
firehoseprotocol that land in S3, records are already newline-delimited by default (NDJSON). Do NOT turn on Firehose'sAppendDelimiterToRecord— 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:
PutTargetswithDeadLetterConfig.Arn=<DLQ>, plus SQS policyAllow sqs:SendMessageforService: events.amazonaws.comwithaws:SourceArn= the rule ARN. SNS:SetSubscriptionAttributesRedrivePolicy={"deadLetterTargetArn":"<DLQ>"}, plus SQS policy allowingService: sns.amazonaws.comscoped 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,SetQueueAttributeswithReceiveMessageWaitTimeSeconds=20(long polling) andKmsMasterKeyId=<customer-managed key id/ARN>rather than leavingalias/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 theAmazonMSK_prefix and encrypted with a customer-managed KMS key (secrets created with the defaultaws/secretsmanagerkey cannot be associated with a cluster), then attached viaBatchAssociateScramSecret. 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:SourceAccountconditions. When a queue or topic policy grants a service principal likeevents.amazonaws.com,sns.amazonaws.com, ors3.amazonaws.compermission tosqs:SendMessageorsns: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 withaws:SourceArn(the specific rule/topic/bucket/pipe ARN; useArnLikewith*when the ARN isn't fully known yet) andaws:SourceAccount(your account ID). For S3 event notifications both keys are required because S3 bucket ARNs don't carry the account ID, soaws:SourceArnalone doesn't constrain the account. The same pattern applies to role trust policies for IAM roles used by EventBridge rules and EventBridge Pipes (principalevents.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.
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server
Security
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Attribution and licence
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- aws/agent-toolkit-for-aws · skills/core-skills/aws-messaging-and-streaming · commit 0a218f8
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- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
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