DynamoDB Streams enable __________ processing of data changes in DynamoDB tables.
- Batch
- Delayed
- Periodic
- Real-time
DynamoDB Streams enable real-time processing of data changes in DynamoDB tables, allowing immediate and continuous data handling.
To consume DynamoDB Streams in real-time, you can use services like __________ or AWS Lambda.
- AWS EC2
- AWS S3
- Amazon Kinesis
- Amazon Redshift
Amazon Kinesis can consume DynamoDB Streams in real-time, providing a way to process and analyze streaming data.
__________ is a mechanism provided by DynamoDB Streams to ensure that each shards data is processed in the correct order.
- Partition keys
- Sequence numbers
- Shard iterators
- Stream records
Sequence numbers in DynamoDB Streams ensure that records within a shard are processed in the correct order, maintaining data consistency.
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.