G2

Atharva P.

Cloud BI Engineer

Enterprise (> 1000 emp.)

7/30/2026

"Streamlined End-to-End IoT Analytics with Seamless QuickSight and SageMaker Integration"

4.5/5

What do you like best about AWS IoT Analytics?

We previously used AWS IoT Analytics to process IoT telemetry. Since AWS has closed the service to new customers, newer architectures increasingly rely on AWS IoT Core, Amazon Kinesis, AWS Glue, Amazon Timestream, Amazon S3, and Amazon Athena to support analytics workflows.

AWS IoT Analytics streamlined the end-to-end IoT analytics pipeline with Channels, Pipelines, Data Stores, Datasets, and built-in SQL transformations. Rather than building ETL pipelines manually, we could handle data cleansing, filtering, enrichment, and analysis within a single managed service.

It also integrated smoothly with QuickSight for dashboards and SageMaker for machine learning, which reduced development effort for operational analytics. Review collected by and hosted on G2.com.

What do you dislike about AWS IoT Analytics?

It was mainly designed for IoT workloads rather than broader, general-purpose analytics, which made it less flexible than modern lakehouse architectures built on S3 and Glue. As AWS moved toward more comprehensive analytics services, migrating away from it became necessary. Review collected by and hosted on G2.com.

What problems is AWS IoT Analytics solving and how is that benefiting you?

IoT Analytics simplified the collection, cleaning, and analysis of large volumes of sensor data.

For example, telemetry from industrial devices first entered IoT Core and then flowed through IoT Analytics pipelines for filtering, enrichment, and aggregation. The processed datasets were visualised in QuickSight and also used by SageMaker to build predictive maintenance models, which reduced engineering effort and helped accelerate insights. Review collected by and hosted on G2.com.