AWS SQS + AWS SNS
Integrate AWS SQS and AWS SNS to Build Resilient, Scalable Messaging Pipelines
Put SNS fan-out and SQS queuing together to get fault-tolerant, event-driven architectures running on tray.ai.
Why integrate AWS SQS and AWS SNS?
AWS SNS and AWS SQS are naturally complementary. SNS broadcasts messages to multiple subscribers in real time; SQS buffers and decouples those messages for reliable, asynchronous processing. Together, they're the backbone of most event-driven architectures, letting teams build scalable workflows without dropping messages. On tray.ai, you can orchestrate both services alongside your broader business tool stack to automate complex multi-step workflows with no custom infrastructure required.
Automate & integrate AWS SQS & AWS SNS
Use case
Fan-Out Event Processing Across Multiple Microservices
When a business event fires — a new order, a user sign-up, a payment confirmation — publish it to an SNS topic and fan the message out to multiple SQS queues, each serving a different microservice or downstream system. tray.ai orchestrates the entire flow so every consumer receives and processes the event independently without blocking others.
Use case
Dead-Letter Queue Monitoring and Alerting
Configure tray.ai to continuously poll SQS Dead-Letter Queues (DLQs) that collect messages failing SNS-triggered processing. When messages land in the DLQ, tray.ai parses the failure context, logs details to a monitoring tool like Datadog or PagerDuty, and notifies on-call engineers via Slack or email before issues escalate.
Use case
Cross-Account and Cross-Region Event Routing
Many enterprises run workloads across multiple AWS accounts and regions, which means messages published to an SNS topic in one account need to be reliably consumed from an SQS queue in another. tray.ai handles this cross-boundary routing, applying business logic, transformations, and conditional filtering as messages move across account or region boundaries.
Use case
Real-Time Data Pipeline Triggering for Analytics and Data Warehousing
Use SNS to capture data-change events from upstream applications and route them through SQS queues into tray.ai workflows that load, transform, and upsert records into data warehouses like Snowflake, BigQuery, or Redshift. This replaces fragile batch jobs with a continuous, event-driven ingestion pipeline that keeps analytical data fresh.
Use case
Automated Incident Response and Runbook Execution
When AWS CloudWatch alarms publish alerts to SNS topics, tray.ai can consume those events from subscribed SQS queues and automatically execute incident-response runbooks — restarting services, scaling Auto Scaling Groups, opening tickets in Jira or ServiceNow, and paging the on-call team, all within seconds of the alarm firing.
Use case
Order and Transaction Workflow Orchestration
E-commerce and fintech platforms can use SNS to broadcast order or transaction events, with tray.ai routing them through dedicated SQS queues for inventory reservation, fraud scoring, payment settlement, and customer notification — each step processed in the correct sequence with retry logic built in.
Use case
Multi-Tenant SaaS Event Isolation and Routing
SaaS platforms serving multiple customers can use SNS message attributes and filtering policies to route tenant-specific events to dedicated SQS queues on tray.ai, keeping data strictly isolated and enabling per-tenant workflow customization without duplicating infrastructure.
Get started with AWS SQS & AWS SNS integration today
AWS SQS & AWS SNS Challenges
What challenges are there when working with AWS SQS & AWS SNS and how will using Tray.ai help?
Challenge
Message Ordering and Exactly-Once Processing Guarantees
Standard SQS queues don't guarantee message ordering, and SNS fan-out can result in the same logical event being processed multiple times if consumers aren't idempotent. Engineering teams often spend significant effort building deduplication logic and sequencing guards from scratch.
How Tray.ai Can Help:
tray.ai workflows natively support FIFO SQS queue integration for ordered processing and include built-in deduplication key handling. Workflow logic can be configured to check for duplicate message IDs before executing downstream actions, and conditional branches handle out-of-order events without custom code.
Challenge
Visibility into End-to-End Message Flow Across Services
When messages traverse SNS topics, multiple SQS queues, and downstream processors, diagnosing where a failure or delay occurred is genuinely hard. Engineers end up manually correlating CloudWatch metrics, SQS ApproximateAgeOfOldestMessage, and application logs to trace a single message's journey.
How Tray.ai Can Help:
tray.ai provides full workflow execution logs with timestamped step-level detail for every message processed, so teams can trace any message from SNS publication through SQS consumption to final downstream action in a single view. Built-in alerting fires when queue depth thresholds or processing latencies are exceeded.
Challenge
Handling Message Schema Evolution Without Breaking Consumers
As upstream services evolve, SNS message payloads frequently change — fields get added, renamed, or removed — and downstream SQS consumers built with rigid schemas break silently, causing data loss or processing errors that are hard to detect until real damage is done.
How Tray.ai Can Help:
tray.ai's visual data mapper lets teams define flexible field mappings with default values, optional field handling, and conditional transformation logic that accommodates schema variations without breaking. When an unexpected schema is detected, workflows can route the message to a holding queue and alert the engineering team rather than silently dropping data.
Challenge
Managing IAM Permissions and Cross-Account Queue Policies
Subscribing SQS queues to SNS topics — especially across AWS accounts — requires precise IAM role configurations, SQS access policies, and SNS topic policies that are error-prone to set up and maintain. Misconfigured policies silently block message delivery with no obvious error surfaced to the developer.
How Tray.ai Can Help:
tray.ai's AWS connectors use a single, centrally managed IAM role configuration that's validated at connection setup time, with clear error messages when permission boundaries block an operation. Cross-account workflows are configured once through tray.ai's secure credential management, which abstracts away the underlying policy complexity and reduces the chance of misconfiguration.
Challenge
Scaling Workflow Throughput to Match Variable SNS Message Bursts
SNS can deliver messages to SQS queues in large, sudden bursts during traffic spikes — flash sales, batch job completions, incident storms — overwhelming downstream consumers sized for average load and causing queue depth to spiral upward.
How Tray.ai Can Help:
tray.ai automatically scales workflow execution concurrency in response to SQS queue depth, processing multiple messages in parallel without requiring teams to pre-provision or manually tune worker capacity. Per-step throttle settings and built-in rate-limiting controls ensure downstream APIs and databases aren't overwhelmed even when queue depth spikes dramatically.
Start using our pre-built AWS SQS & AWS SNS templates today
Start from scratch or use one of our pre-built AWS SQS & AWS SNS templates to quickly solve your most common use cases.
AWS SQS & AWS SNS Templates
Find pre-built AWS SQS & AWS SNS solutions for common use cases
Template
SNS Topic to SQS Queue Fan-Out with Multi-System Notification
Automatically receives messages published to an SNS topic, fans them out to multiple SQS queues, and triggers downstream actions in Slack, Jira, and a database — giving every team their own reliable message stream from a single event source.
Steps:
- Trigger: Message published to a specified AWS SNS topic
- Fan out: tray.ai delivers the SNS message to two or more subscribed SQS queues based on filter policy attributes
- Process: Poll each SQS queue independently and parse message body and metadata
- Notify: Post a structured summary to a Slack channel for ops visibility
- Record: Upsert a log record into a database or data warehouse for auditing
Connectors Used: AWS SNS, AWS SQS
Template
SQS Dead-Letter Queue Alert and Auto-Remediation
Monitors an SQS Dead-Letter Queue populated by failed SNS-triggered processing jobs, sends structured alerts to PagerDuty and Slack when failures are detected, and optionally replays correctable messages back to the source queue after applying a fix.
Steps:
- Trigger: Scheduled or continuous poll of the SQS Dead-Letter Queue
- Evaluate: Parse each failed message to extract error type, source SNS topic, and payload
- Alert: Create a PagerDuty incident and post failure details to a Slack ops channel
- Remediate: If the error is retriable, delete from DLQ and republish to the original SNS topic or SQS queue
- Log: Write failure and resolution details to a monitoring dashboard or S3 bucket
Connectors Used: AWS SQS, AWS SNS
Template
CloudWatch Alarm to SNS to SQS Incident Runbook
Captures CloudWatch alarm notifications delivered to an SNS topic, routes them through an SQS queue for reliable processing, and executes a predefined incident-response runbook including Jira ticket creation, Slack paging, and optional AWS API remediation actions.
Steps:
- Trigger: CloudWatch Alarm publishes alert payload to an SNS topic
- Queue: SNS delivers the alarm message to a subscribed SQS queue for durable processing
- Parse: Extract alarm name, affected resource, and severity from the SQS message body
- Ticket: Create a Jira incident ticket with full alarm context and assign to on-call engineer
- Notify: Send a formatted Slack message to the incident channel with a direct link to the Jira ticket
Connectors Used: AWS SNS, AWS SQS
Template
Real-Time SNS Event to Data Warehouse Ingestion Pipeline
Listens for data-change events on an SNS topic, buffers them in an SQS queue for ordered processing, transforms field names and data types, and upserts records into Snowflake or BigQuery to keep analytical tables continuously up to date.
Steps:
- Trigger: New message arrives on an SQS queue subscribed to an SNS data-change topic
- Validate: Check message schema and reject or quarantine malformed payloads to the DLQ
- Transform: Map source fields to target schema, cast data types, and enrich with metadata
- Upsert: Insert or update the transformed record in Snowflake, BigQuery, or Redshift
- Acknowledge: Delete the successfully processed message from the SQS queue
Connectors Used: AWS SNS, AWS SQS
Template
Multi-Tenant Event Routing with SNS Filter Policies
Routes multi-tenant SaaS events published to a shared SNS topic into tenant-specific SQS queues using message attribute filter policies, then executes per-tenant workflows including CRM updates, email notifications, and billing system syncs.
Steps:
- Publish: Application publishes event to shared SNS topic with tenant ID as a message attribute
- Filter: SNS filter policies route the message to the correct tenant-specific SQS queue
- Consume: tray.ai polls each tenant SQS queue and deserializes the event payload
- Sync: Update the tenant's CRM record in Salesforce or HubSpot with event data
- Notify: Send a tenant-branded email or in-app notification confirming the event action
Connectors Used: AWS SNS, AWS SQS
Template
Order Event Fan-Out for E-Commerce Workflow Orchestration
Publishes order-placed events to an SNS topic, fans them out to dedicated SQS queues for inventory, fraud, payment, and notifications, and orchestrates each downstream step with retry logic and status tracking in a central database.
Steps:
- Trigger: Order service publishes order-placed event to SNS topic
- Fan out: SNS delivers message to four SQS queues — inventory, fraud, payment, and notifications
- Process: tray.ai polls each queue and calls the respective downstream API with retry logic
- Aggregate: Update a central order-status record in a database as each step completes
- Notify: Send order confirmation email or SMS to customer once payment queue confirms success
Connectors Used: AWS SNS, AWS SQS