What is a common use case for integrating SNS with AWS Lambda?
- Long-term data storage
- Machine learning model training
- Managing network traffic
- Processing real-time events
Integrating SNS with AWS Lambda allows for processing real-time events, such as notifications, updates, or alerts, with serverless functions.
What are the main benefits of using SNS triggers with AWS Lambda functions?
- Asynchronous event processing
- Cost optimization
- Load balancing
- Real-time data processing
SNS triggers enable AWS Lambda functions to process events asynchronously, allowing for scalable and responsive event-driven architectures.
How does AWS Lambda ensure the reliability of SNS triggers?
- By automatically retrying failed invocations
- By buffering messages before processing
- By delaying message delivery
- By discarding failed messages
AWS Lambda ensures the reliability of SNS triggers by automatically retrying failed invocations, ensuring that messages are processed reliably.
What are some potential challenges when using SNS triggers with AWS Lambda, and how can they be addressed?
- IAM permission errors
- Message format compatibility issues
- Network latency
- Throttling and concurrency limitations
Throttling and concurrency limitations can occur when using SNS triggers with AWS Lambda, but they can be addressed by optimizing function performance or adjusting concurrency settings.
How does the AWS Lambda execution environment process SNS messages when triggered?
- By directly receiving messages from SNS brokers
- By invoking the specified Lambda function asynchronously
- By invoking the specified Lambda function synchronously
- By polling the SNS topic for messages
When triggered by SNS messages, AWS Lambda invokes the specified function asynchronously, processing the messages in the background.
Scenario: You need to implement a serverless architecture where incoming data from IoT devices triggers AWS Lambda functions for processing. How would you design the integration between SNS and AWS Lambda in this scenario?
- Deploy EC2 instances
- Publish data to SNS topic
- Use S3 for data storage
- Utilize Kinesis Data Streams
Publishing data to an SNS topic allows SNS to trigger AWS Lambda functions for processing the incoming data from IoT devices.
What is DynamoDB Streams primarily used for?
- Automating backups
- Capturing data modification events
- Ensuring high availability
- Managing database schema
DynamoDB Streams captures data modification events in a DynamoDB table, allowing you to track changes and trigger actions based on those changes.
How does DynamoDB Streams ensure data durability?
- By creating backups
- By replicating data across multiple regions
- By storing data in memory
- By writing data to disk
DynamoDB Streams ensure data durability by replicating data across multiple availability zones to prevent data loss.
In DynamoDB Streams, what triggers the generation of stream records?
- Data modifications (create, update, delete)
- Read operations
- Schema changes
- Table scans
Stream records in DynamoDB Streams are generated when data modifications such as create, update, and delete operations occur in the table.
How long does DynamoDB Streams retain records by default?
- 24 hours
- 30 days
- 48 hours
- 7 days
DynamoDB Streams retains records for 24 hours by default.