AWS DynamoDB Change Data Capture (CDC) with Lambda Processing Pattern

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An AWS DynamoDB Change Data Capture (CDC) with Lambda Processing Pattern is a CDC pattern where DynamoDB Streams detects table changes and triggers AWS Lambda functions which process these changes as an event-driven response mechanism.

  • Context:
    • It enables real-time capture and processing of data modifications in a DynamoDB table, turning each data change into a discrete event for immediate action.
    • It integrates with other AWS services to facilitate complex workflows, such as data enrichment, aggregation, and subsequent updates to other systems or databases.
    • It offloads the heavy lifting of managing infrastructure scalability to AWS, with Lambda automatically scaling to match the volume of events and DynamoDB handling the throughput of change data.
    • …
  • Example(s):
    • As demonstrated in a real-time fraud detection system, DynamoDB captures transaction data that triggers Lambda to perform immediate fraud risk analysis.
    • As demonstrated within inventory management applications, updates to inventory levels in DynamoDB prompt Lambda functions to ensure real-time data accuracy across supply chains.
    • As demonstrated in a dynamic notification system, social interactions logged in DynamoDB initiate Lambda processes to send instant notifications through multiple channels.
    • As demonstrated in a Content Moderation Workflow, where user-generated content changes captured by DynamoDB trigger Lambda to call a large language model for in-depth analysis, despite the operation taking up to a minute, efficiently handling potential API timeouts.
    • …
  • Counter-Example(s):
  • See: Amazon DynamoDB, AWS Lambda, Event-Driven Architecture, Serverless Computing, Real-Time Computing, Near-Real-Time Data Processing.

References

2023

  • Bing Chat
    • … The stream is provided by the DynamoDB NoSQL database service. The stream captures the changes made to the data in a DynamoDB table and delivers them to the subscribers. The subscribers are AWS Lambda functions, which are pieces of code that run in response to events. The Lambda functions can perform any logic or action based on the messages, such as sending notifications, updating other services, or generating reports. ...

2023

  • chat
    • The Publish/Subscribe Messaging Pattern is inherently a decoupled communication model where the publishers and subscribers do not know about the presence of each other. AWS services like Amazon SNS and Amazon SQS are more directly suited for this pattern. However, you can mimic this pattern using DynamoDB, DynamoDB Streams, and AWS Lambda by leveraging the event-driven architecture of AWS.
    • Here’s how you could architect the Publish/Subscribe pattern using these services:
      • Publish Phase:
        • Producers/Publishers:
          • Application components or services that generate messages.
          • These producers write messages to a DynamoDB table, specifically designed to capture these events or messages.
          • Each record represents a message that would be published.
      • Broker Phase:
        • DynamoDB Streams:
          • Acts as a broker by capturing the new records inserted into the DynamoDB table.
          • The stream ensures the ordered delivery of new messages, typically within milliseconds of the actual database update.
      • Subscribe Phase:
        • AWS Lambda (Subscribers):
          • One or more Lambda functions are configured to be triggered by the DynamoDB Stream events.
          • Each Lambda function can represent a different subscriber in the pub/sub model.
          • The Lambda functions receive the new records as event payloads and process them accordingly.
          • Each function can have its own processing logic, output destination, and error handling procedures.
      • Message Filtering:
        • In a typical pub/sub system, subscribers can often subscribe to specific types of messages. While DynamoDB Streams doesn't directly support this, you could implement filtering logic within the Lambda functions to process only the messages of interest. Alternatively, you could route the stream through an intermediary service that provides filtering capabilities before invoking the Lambda functions.
      • Processing & Error Handling:
        • AWS Lambda:
          • Processes messages and can perform a variety of actions such as updating another DynamoDB table, invoking another AWS service, sending a notification, or even calling external APIs.
          • Should include error handling logic to deal with processing failures, possibly using dead-letter queues (DLQs) or other retry mechanisms.
      • Scaling and Parallel Processing:
        • The combination of DynamoDB Streams and AWS Lambda can scale automatically to handle varying loads.
        • Lambda functions can process records in batches, and you can configure the batch size and the concurrency settings to optimize throughput and performance.
      • Fan-Out Scenario:
        • In scenarios where the same message needs to be processed by multiple, independent Lambda functions (a fan-out), you can configure multiple streams (via stream ARNs) to trigger different Lambda functions.
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