AWS Messaging And Streaming

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

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.

Streaming Services

These services are generally used for streaming workloads.

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.

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.