AWS IoT Analytics Is Discontinued - Migration Guide for IoT Data Analytics Pipelines
AWS IoT Analytics ended on December 15, 2025. This article covers the official migration targets (Kinesis Data Streams / Data Firehose, S3 / Timestream, Glue + Athena) and its original four-component architecture.
End of Support and Migration Paths
AWS IoT Analytics reached its end of support on December 15, 2025, and had stopped accepting new customers on July 24, 2024. The console and service resources are no longer accessible. The official AWS migration guide maps each capability to alternative services: Amazon Kinesis Data Streams or Amazon Data Firehose for data collection, Amazon Managed Service for Apache Flink or Data Firehose with Lambda for processing, Amazon S3 or Amazon Timestream for storage, and AWS Glue with Amazon Athena for analysis. This page remains available as a historical reference and migration guide.
IoT Data Analytics Challenges and the Role of IoT Analytics
IoT devices continuously send large volumes of telemetry data - temperature, humidity, vibration, location, operational status, and more. Analyzing this data requires preprocessing such as noise removal, missing value imputation, unit conversion, and outlier filtering. While it's possible to receive data with IoT Core, preprocess it with Lambda, store it in DynamoDB or S3, and query it with Athena, you'd need to build and manage the integration between each component yourself. AWS IoT Analytics provides a managed service for the entire pipeline from data collection to analysis. It consists of four components - channels (data ingestion), pipelines (preprocessing), data stores (storage), and datasets (query results) - automatically processing, accumulating, and analyzing data from IoT Core.
The Four-Component Architecture
Channels are the entry point that receives data from IoT Core rule actions or the BatchPutMessage API. Raw data is stored as-is and can be used for reprocessing. Pipelines retrieve data from channels and apply a series of activities (processing steps). Built-in activities include adding/removing attributes, filtering (excluding data that doesn't meet conditions), mathematical transformations (unit conversion), and adding metadata from the device registry. Lambda activities let you run custom preprocessing logic. Data stores accumulate pipeline-processed data, using S3 buckets as the backend with support for Parquet format storage. Setting a retention period automatically deletes old data. Datasets are saved SQL query results that can be scheduled to refresh periodically (hourly, daily, etc.).
Analytics and Visualization
Dataset SQL queries run aggregation, filtering, and join operations against data store contents. For example, you can define queries like "average and maximum temperature per device over the past 24 hours" or "list of devices where anomalies (threshold breaches) occurred" and schedule them for periodic refresh. Dataset results connect directly to QuickSight for building dashboards that visualize device operational status, sensor value trends, and anomaly detection alerts in real time. Jupyter Notebook integration lets you access data store contents directly from SageMaker notebook instances for building and validating ML models. This enables ML use cases like predictive maintenance (equipment failure prediction), anomaly detection, and demand forecasting. A feature for running containerized analysis code as dataset actions also lets you build periodic ML inference pipelines.
Pricing and Choosing Between IoT Analytics and Timestream
With the shutdown, the following pricing no longer exists (kept as a historical record). IoT Analytics pricing includes message processing (pipeline) at $0.20 per million messages, data storage at $0.03/GB/month, and queries at $5.00 per TB of data analyzed. When choosing between IoT Analytics and Timestream, IoT Analytics is the better fit when you need a data preprocessing pipeline (filtering, transformation, enrichment). Timestream is better suited for fast querying and aggregation of pre-processed time series data. A combined approach - preprocessing data with IoT Analytics pipelines and writing it to Timestream for real-time queries - is also effective. For small-scale IoT projects, IoT Analytics alone is sufficient, but consider adding Timestream when you need real-time queries across a large number of devices.
Summary - Migration Guidelines
AWS IoT Analytics offered a managed pipeline for collecting, preprocessing, and analyzing IoT device data, but it reached end of support on December 15, 2025. To build an equivalent analytics foundation today, follow the official migration guide: Kinesis Data Streams / Data Firehose for collection, Managed Service for Apache Flink or Data Firehose with Lambda for preprocessing, S3 / Timestream for storage, and Glue + Athena for analysis. The design principle in this article, ensuring data quality through preprocessing, still applies to the replacement architecture.
References (Official AWS Resources)
The primary sources for this page are the official AWS website and documentation. Check the official pages below for the latest specifications and pricing.
- AWS IoT Analytics official page
- AWS IoT Core official page
- Amazon Quick official page
- AWS Documentation (official)
If this page and the official documentation disagree, treat the official documentation as authoritative.