AWS DVA-C02 Drill: DynamoDB Throttling - Decoupling with SQS to Avoid Latency Spikes

Jeff’s Note

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Unlike generic exam dumps, ADH analyzes this scenario through the lens of a Real-World Lead Developer.

For DVA-C02 candidates, the confusion often lies in understanding how to architect Lambda-to-DynamoDB workflows that gracefully handle traffic spikes without throttling. In production, this is about knowing exactly when to decouple workloads to smooth out bursts using messaging services like SQS versus when to rely on auto scaling or provisioned concurrency. Let’s drill down.

The Certification Drill (Simulated Question)

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Scenario

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DataWave Labs has built a serverless analytics ingestion pipeline. Incoming JSON payloads are stored as objects in an Amazon S3 bucket. A Lambda function automatically triggers to transform this data and then writes the processed results into a DynamoDB table. Recently, the application has experienced sudden and unpredictable surges of incoming data traffic. This has caused throttling issues on the DynamoDB table, resulting in delays and occasional failures during writes. The development team needs a robust solution to eliminate throttling and ensure smooth, consistent data loads into DynamoDB despite traffic fluctuations.

The Requirement:

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Design a solution to prevent DynamoDB write throttling caused by sudden traffic spikes, and enable the Lambda function’s data load to occur consistently.

The Options

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  - A) Refactor the Lambda function into two separate functions. Configure the first function to transform incoming data and send messages to an Amazon Simple Queue Service (SQS) queue. Configure the second function to be triggered by the SQS queue and handle the data writes into the DynamoDB table.
  - B) Enable DynamoDB auto scaling on the table and configure Amazon CloudWatch alarms to monitor read/write capacity and consumed throughput metrics.
  - C) Create an alias for the Lambda function and configure provisioned concurrency to reduce cold starts and improve consistent execution.
  - D) Refactor the Lambda function into two functions where the first writes data into DynamoDB and the second processes DynamoDB Streams to update items after they are stored.

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Correct Answer

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A

Quick Insight: The Developer Imperative

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Lambda to DynamoDB workflows can hit throttling if bursts exceed table capacity. Decoupling ingestion from storage via SQS buffers traffic and smooths spikes, preventing throttling and handling retries gracefully. Direct scaling or provisioned concurrency helps but may not eliminate bursts during extreme spikes as effectively.

Content Locked: The Expert Analysis

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You’ve identified the answer. But do you know the implementation details that separate a Junior from a Senior?

The Expert’s Analysis

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Correct Answer

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Option A

The Winning Logic

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Option A correctly applies a proven, scalable pattern by splitting responsibilities across two Lambda functions connected by an SQS queue. The first Lambda transforms and queues messages, decoupling ingestion from write operations. The second Lambda processes SQS messages at a controlled rate, writing to DynamoDB, which smooths out bursty traffic and helps avoid throttling. SQS serves as a reliable buffer layer that supports retries and error handling.

• This architecture handles variable workloads without overwhelming DynamoDB capacity.
• It enables smoother, more consistent write throughput.
• It aligns with serverless best practices of decoupling, enhancing scalability and resiliency.

Option B (Auto Scaling) can help but may lag behind immediate bursts causing throttling before scaling effects occur. Option C (Provisioned Concurrency) reduces cold starts but does not address throughput throttling at DynamoDB. Option D uses DynamoDB Streams for eventual updates, but this does not solve throttling on the initial writes.

The Trap (Distractor Analysis):

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• Why not B? Auto scaling helps dynamically adjust throughput but may not respond fast enough to sudden spikes causing throttling and errors. It also adds some complexity and cost overhead.

• Why not C? Provisioned concurrency optimizes Lambda startup latency but does nothing to increase DynamoDB write capacity or handle throttling.

• Why not D? Using a secondary function triggered by DynamoDB Streams to update items after writes does not prevent throttling during the initial write operation itself. It adds unnecessary complexity.

The Technical Blueprint

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• For Developer / SysOps (Code/CLI Snippet):

# Create SQS queue (standard)
aws sqs create-queue --queue-name datawave-processing-queue

# In first Lambda: send message to SQS after transforming data
aws sqs send-message --queue-url https://sqs.region.amazonaws.com/account-id/datawave-processing-queue --message-body file://transformed-data.json

# Configure second Lambda with SQS event source mapping
aws lambda create-event-source-mapping --function-name DataWave-DynamoWriteFunction \
  --batch-size 10 --event-source-arn arn:aws:sqs:region:account-id:datawave-processing-queue

The Comparative Analysis

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Real-World Application (Practitioner Insight)

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Exam Rule

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For the exam, always pick decoupling with SQS when you see “throttling due to uneven or bursty traffic” in a Lambda to DynamoDB write scenario.

Real World

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In reality, teams might combine both SQS-based decoupling AND enabling auto scaling to get fast adaptation plus buffering. Provisioned concurrency is primarily for Lambda performance improvements, not throughput scaling.

(CTA) Stop Guessing, Start Mastering

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Disclaimer

This is a study note based on simulated scenarios for the AWS DVA-C02 exam.