DynamoDB Streams are triggered by changes to __________ tables.
- DynamoDB
- RDS
- Redshift
- S3
DynamoDB Streams are triggered by changes to DynamoDB tables, capturing data modifications and enabling subsequent processing.
__________ is the process of capturing a time-ordered sequence of item-level modifications in a DynamoDB table.
- Change data capture
- Data replication
- Data warehousing
- ETL
Change data capture is the process of capturing a time-ordered sequence of item-level modifications in a DynamoDB table.
DynamoDB Streams provide an at-least-once __________ of stream records, ensuring durability and data consistency.
- Delivery
- Execution
- Processing
- Retransmission
DynamoDB Streams ensure at-least-once delivery of stream records, meaning each record is delivered to the consumer at least once, ensuring durability and data consistency.
Scenario: You are designing an application where you need to perform real-time analytics on data changes in a DynamoDB table. How would you implement this using DynamoDB Streams and AWS Lambda?
- Create a Lambda function triggered by DynamoDB Streams
- Directly query the DynamoDB table for changes
- Schedule periodic batch jobs with Lambda
- Use AWS Glue for ETL jobs
Creating a Lambda function triggered by DynamoDB Streams allows you to process changes in real time, enabling real-time analytics.
Scenario: Your team is building a system where data integrity is crucial, and you're considering using DynamoDB Streams for change tracking. What are some considerations you need to keep in mind regarding data consistency and reliability?
- Ensure idempotency in Lambda functions
- Ignore duplicate records
- Rely on DynamoDB's default retry behavior
- Use eventual consistency for all operations
Ensuring idempotency in Lambda functions is crucial to maintain data integrity and reliability when using DynamoDB Streams for change tracking.
Scenario: You're tasked with building a scalable and fault-tolerant system using DynamoDB Streams for a high-traffic application. How would you design the system to handle potential spikes in workload and ensure reliable processing of stream records?
- Depend on DynamoDB auto-scaling only
- Implement a dead-letter queue for failed records
- Limit the number of stream records processed
- Use a single large Lambda function
Implementing a dead-letter queue for failed records ensures that any unprocessed records are not lost, allowing for reliable and fault-tolerant processing.
What are the key components of an AWS Lambda function?
- API Gateway, CloudWatch, S3 bucket
- EC2 instances, Load balancer, Auto Scaling group
- Function code, Runtime, Handler
- Function name, IAM role, Event source
The key components of an AWS Lambda function include the function code, runtime, and handler.
How does AWS Lambda pricing typically work?
- Fixed monthly subscription
- Pay-per-invocation
- Pay-per-storage
- Pay-per-use
AWS Lambda pricing typically works on a pay-per-use model, where you are charged for the compute time consumed by your function.
How does AWS Lambda handle scaling automatically?
- Automatically adjusts based on incoming traffic
- Relies on third-party tools for scaling
- Requires manual intervention for scaling
- Uses static scaling configurations
AWS Lambda automatically adjusts its capacity to handle incoming traffic, scaling up or down as needed to accommodate changes in demand.
What are some benefits of using AWS Lambda for serverless computing?
- High upfront costs
- Limited language support
- Reduced operational overhead
- Requires manual scaling
AWS Lambda reduces operational overhead by automatically managing server provisioning, maintenance, and scaling, allowing developers to focus on code development.