Which ETL service reliably captures, transforms, and delivers streaming data to data lakes, data stores, and analytics services?

• A. Amazon Kinesis Data Streams
• B. Amazon MSK
• C. Amazon Kinesis Data Firehose
• D. AWS Data Pipeline

Answer: (C)

The essential idea here is a service that handles streaming data end-to-end for ETL: it captures the stream, can transform the data on the fly, and reliably delivers it to data lakes, data stores, or analytics services. Amazon Kinesis Data Firehose is built for this pattern. It is a fully managed, serverless service that ingests streaming records and delivers them directly to destinations such as Amazon S3 for data lakes, Redshift for data warehousing, Elasticsearch Service for analytics, or Splunk. It can perform transformations by integrating with a Lambda function, allowing you to modify records as they pass through, before they’re stored or analyzed. Firehose also handles buffering, retries, and automatic scaling, ensuring reliable delivery without you having to manage infrastructure. In contrast, a pure streaming ingestion service like Kinesis Data Streams requires separate processing to transform and load data, a managed Kafka service like MSK focuses on broker-level streaming, and a workflow tool like AWS Data Pipeline is geared toward batch processing rather than continuous streaming ETL.