DEA-C01 Question 18
Select 2A company collects clickstream data from its e-commerce website and wants to analyze user behavior in near real-time. The data arrives continuously at a high velocity and must be ingested with minimal latency. Additionally, the company wants to retain historical data for long-term analysis. Which combination of AWS services and ingestion patterns would best meet these requirements?
- A
Use Amazon Kinesis Data Streams for real-time ingestion and Amazon S3 for storing historical data.
- B
Use AWS Glue to ingest real-time data and Amazon RDS for storing historical data.
- C
Use Amazon Kafka for ingesting real-time data and Amazon Redshift for storing historical data.
- D
Use Amazon Kinesis Data Firehose to stream real-time data to Amazon S3, and query it using Amazon Athena for historical analysis.
- E
Use Amazon SQS for real-time ingestion and Amazon DynamoDB for storing historical data.
Show answer and explanation
Correct answers: A, D
Explanation
To meet the requirements of near real-time ingestion and long-term historical data storage, the best approach is to use Amazon Kinesis Data Streams or Amazon Kinesis Data Firehose. Kinesis Data Streams is optimized for real-time ingestion, while Firehose can stream data directly to Amazon S3. For historical analysis, Amazon S3 provides cost-effective storage, and tools like Amazon Athena can be used to query the data efficiently. Other services like AWS Glue or SQS are not suitable for real-time ingestion, and storage solutions like RDS or DynamoDB do not scale well for large volumes of historical data.
- A. Correct.
Correct: Amazon Kinesis Data Streams is designed for real-time data ingestion with low latency. Amazon S3 is a cost-effective option for storing large volumes of historical data.
- B. Incorrect.
Incorrect: AWS Glue is primarily an ETL service and not optimized for real-time data ingestion. Amazon RDS is not ideal for storing large volumes of historical data due to scaling and cost limitations.
- C. Incorrect.
Incorrect: While Amazon Kafka (self-managed or MSK) supports real-time ingestion, Amazon Redshift is optimized for analytical queries rather than directly ingesting real-time data. Additional processing would be needed.
- D. Correct.
Correct: Amazon Kinesis Data Firehose is a fully managed service that can stream real-time data into Amazon S3. Amazon Athena can query data stored in S3, making it suitable for historical analysis.
- E. Incorrect.
Incorrect: Amazon SQS is a message queuing service, not designed for real-time streaming use cases. Amazon DynamoDB is optimized for low-latency key-value and document storage, not for historical data storage at scale.