G2

Atharva P.

Cloud BI Engineer

Enterprise (> 1000 emp.)

7/30/2026

"Reliable, Low-Ops Streaming Ingestion with Amazon Data Firehose"

4/5

What do you like best about Amazon Kinesis Data Firehose?

We originally adopted Amazon Kinesis Data Firehose. AWS has since renamed the service to Amazon Data Firehose, but the functionality remains the same and we continue using it under its new name.

Amazon Data Firehose greatly simplified streaming data ingestion by automatically delivering data to analytics and storage platforms without managing consumer applications. I particularly like automatic batching, compression, buffering, format conversion, dynamic partitioning, and serverless scaling.

The service requires very little operational effort and integrates naturally with Amazon S3, Redshift, OpenSearch, Iceberg-based data lakes, and third-party observability platforms. Performance has been consistently reliable for high-volume streaming pipelines, making it an excellent choice for building modern lakehouse architectures. Review collected by and hosted on G2.com.

What do you dislike about Amazon Kinesis Data Firehose?

Transformation capabilities are intentionally lightweight, so more complex business logic typically requires AWS Lambda or downstream processing frameworks such as Spark or Flink. Review collected by and hosted on G2.com.

What problems is Amazon Kinesis Data Firehose solving and how is that benefiting you?

Data Firehose eliminated the need to build and maintain custom streaming ingestion services.

Example: Application logs, audit events, and IoT telemetry were continuously delivered into Amazon S3 as Parquet files with automatic partitioning. This significantly reduced ingestion latency while improving downstream query performance in Athena and Redshift. Review collected by and hosted on G2.com.