# Connect AWS SQS to AWS SNS > Put SNS fan-out and SQS queuing together to get fault-tolerant, event-driven architectures running on tray.ai. **Canonical page:** https://tray.ai/connectors/aws-sqs-aws-sns-integrations/ **AWS SQS connector:** https://tray.ai/connectors/aws-sqs-integrations/ **AWS SQS documentation:** https://tray.ai/documentation/connectors/service/aws-sqs ## Overview 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. SNS alone can't guarantee message persistence or ordered processing. SQS alone has no native fan-out mechanism. The SNS-to-SQS pattern solves both problems at once. With tray.ai, you can visually design workflows that publish events to SNS topics, fan them out to multiple SQS queues for different downstream consumers, and trigger follow-on actions in CRMs, databases, data warehouses, monitoring tools, or communication platforms. That cuts out the hand-coded glue code engineering teams typically maintain, reduces the risk of message loss, and gives business and operations teams visibility into every step of the data flow. ## Use cases ### 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. - Decouple producers from consumers so each microservice scales independently - Guarantee message delivery to every downstream queue even during consumer outages - Eliminate point-to-point integrations that create brittle, hard-to-maintain dependencies ### 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. - Catch processing failures in real time before they cause downstream data loss - Automatically route failure alerts to the right team without manual queue inspection - Maintain a full audit trail of failed messages for root-cause analysis and replay ### 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. - Enable secure, policy-compliant message passing between isolated AWS environments - Apply data masking or field-level transformations before messages cross account boundaries - Simplify cross-region disaster recovery and active-active architectures ### 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. - Replace overnight batch ETL jobs with low-latency, event-driven data ingestion - Automatically handle schema mapping and field transformation before warehouse upsert - Scale ingestion throughput dynamically as upstream event volume grows ### 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. - Cut mean time to response (MTTR) by automating first-responder actions - Ensure every alarm triggers a consistent, documented runbook without human intervention - Create a timestamped incident log in your ITSM system for compliance and post-mortems ### 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. - Process each order step in isolation so a fraud-check failure doesn't block inventory updates - Add automatic retries and exponential backoff for transient downstream API failures - Send real-time order-status notifications to customers via email or SMS at each milestone ### 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. - Guarantee tenant data isolation at the messaging layer without separate infrastructure stacks - Apply per-tenant business logic and integrations from a single centralized tray.ai workflow - Onboard new tenants by simply adding SNS filter policies and SQS queue subscriptions ## Templates ### 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. Connectors used: AWS SNS, AWS SQS ### 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. Connectors used: AWS SQS, AWS SNS ### 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. Connectors used: AWS SNS, AWS SQS ### 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. Connectors used: AWS SNS, AWS SQS ### 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. Connectors used: AWS SNS, AWS SQS ### 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. Connectors used: AWS SNS, AWS SQS ## Challenges Tray.ai solves ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ## Learn more - Intelligent Integration: https://tray.ai/platform/intelligent-ipaas/ - Merlin Agent Builder: https://tray.ai/platform/merlin-agent-builder/ - Agent Gateway for MCP: https://tray.ai/platform/agent-gateway/ - Book a demo: https://tray.ai/contact/