How does AWS Lambda integrate with other AWS services?
- Direct API calls
- Manual configuration
- Only through SDKs
- Through event sources
AWS Lambda integrates with other AWS services through event sources, allowing functions to be triggered by events such as file uploads to Amazon S3 or database updates in Amazon DynamoDB.
Environment variables in AWS Lambda can be used to store sensitive information such as __________.
- API keys
- Encryption keys
- HTML code
- Public URLs
Environment variables in AWS Lambda can be used to store sensitive information such as API keys, database credentials, and configuration settings.
AWS Lambda functions can be written in multiple __________ such as Python, Node.js, and Java.
- Frameworks
- IDEs
- Languages
- Platforms
AWS Lambda functions can be written in multiple programming languages such as Python, Node.js, Java, C#, and Go, among others.
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.