DEA-C01 Question 481
Select 3A company is building a data pipeline on AWS to process large amounts of streaming data from IoT devices. The pipeline must ensure low-latency processing and reliability, and the data should be stored in a highly durable format for future analysis. Which combination of AWS services would best meet these requirements?
- A
Amazon Kinesis Data Streams for ingesting the streaming data
- B
AWS Lambda for real-time processing of the data
- C
Amazon S3 for storing the processed data
- D
Amazon RDS for storing the processed data
- E
Amazon Redshift for real-time processing and storage
Show answer and explanation
Correct answers: A, B, C
Explanation
To meet the requirements of low-latency processing, reliability, and durable storage for IoT streaming data, the combination of Amazon Kinesis Data Streams, AWS Lambda, and Amazon S3 is optimal. Kinesis handles the real-time ingestion, Lambda processes the data in near real-time, and S3 ensures durable and scalable storage for future analysis. RDS and Redshift are not suitable for these specific use cases due to their design limitations regarding real-time streaming data.
- A. Correct.
Amazon Kinesis Data Streams is designed for high-throughput streaming data ingestion and ensures low-latency processing, making it an ideal choice for IoT data streams.
- B. Correct.
AWS Lambda enables real-time data processing with serverless compute, ensuring low latency and scalability for processing streaming data.
- C. Correct.
Amazon S3 provides highly durable and scalable storage, making it suitable for storing processed IoT data for future analysis.
- D. Incorrect.
Amazon RDS is a relational database service and is not optimized for storing large-scale processed data from IoT pipelines. It is better suited for transactional data.
- E. Incorrect.
Amazon Redshift is a data warehouse solution and is not designed for real-time data processing. It is more suited for analytical workloads on structured data.