AWS IoT analytics automates all the steps required to run analytics on IoT data. AWS IoT Analytics can accept data from any source like Amazon Kinesis, S3 or other sources, using an ingestion API and is fully integrated with AWS IoT Core so it is easy to collect data and begin performing analytics. First, you define a channel by using MQTT topic filters to specify only the data you want to store and analyze. Once the channel is set up, you configure a pipeline to process your data. The pipeline can perform data transformations, execute conditional statements, and enrich messages with data from external sources.

After processing the data, AWS IoT Analytics stores it in a time-series data store for analysis. Then, you can run ad hoc or scheduled queries using the built-in SQL query engine to answer specific business questions, or perform more sophisticated analysis and machine learning.

Key Features


Ingest data from any source including AWS IoT Core - Ingest data directly from AWS IoT Core to AWS IoT Analytics. Or, use the data ingestion API to send your data to IoT Analytics from Amazon S3, Amazon Kinesis or any other source. With IoT Analytics' full integration with AWS IoT Core and the API, it is easy to receive messages from connected devices as they stream in.

Collect only the data you want to store and analyze – You use the AWS IoT Analytics console to configure AWS IoT Analytics to receive messages from devices through MQTT topic filters in various formats and frequencies. IoT Analytics validates that the data is within specific parameters you define and creates channels. Then the service routes the channels to appropriate pipelines for message processing, transformation, and enrichment.


Cleanse and filter – AWS IoT Analytics let you define AWS Lambda functions that can be triggers on when IoT Analytics detects missing data, so you can run code to estimate and fill gaps. You can also define max/min filters and percentile thresholds to remove outliers in your data.

Transform – AWS IoT Analytics can transform messages using mathematical or conditional logic you define, so you can perform common calculations like Celsius into Fahrenheit conversion.
Enrich – AWS IoT Analytics can enrich data with external data sources such as a weather forecast information, and then route the data to the IoT Analytics data store.
Reprocess – AWS IoT Analytics can re-process raw data from the Channel connected to the Pipeline. Reprocessing your raw data gives you the flexibility to create a new pipeline or revisit an older pipeline so you can capture new and historical data, make changes to your pipeline, or simply process your data in a different way. This capability is often needed to gain deeper insights or test hypothesis. Simply connect the Pipeline to the appropriate Channel to reprocess.


Time-Series Data Store - AWS IoT Analytics stores the device data in an IoT optimized time-series data store for analysis. You can manage access permissions, implement data retention policies and export your data to external access points.

Store Processed and Raw Data - AWS IoT Analytics stores the processed data and also automatically stores the raw ingested data so you can process it at a later time.


Run Ad hoc or Scheduled SQL Queries - AWS IoT Analytics provides a built-in SQL query engine so you can run ad hoc or scheduled queries and get results quickly. For example, you may want to run a quick query to find out how many monthly active users there are for each device in your fleet.

Time-Series Analysis - AWS IoT Analytics supports time-series analysis so you can analyze the performance of devices over time and understand how and where they are being used, continuously monitor device data to predict maintenance issues, and monitor sensors to predict and react to environmental conditions.

Hosted Notebooks for Sophisticated Analytics and Machine Learning - AWS IoT Analytics includes support for hosted in Jupyter Notebooks for statistical analysis and machine learning. The service includes a set of pre-built notebook templates that contain AWS-authored machine learning models and visualizations to help you get started with IoT use cases related to device failure profiling, forecasting events such as low usage that might signal the customer will abandon the product, or segmenting devices by customer usage levels (for example heavy users, weekend users) or device health.

You can do statistical classification through a method called logistic regression. You can also use Long-Short-Term Memory (LSTM) which is a powerful neural network technique for predicting the output or state of a process that varies over time. The pre-built notebook templates also support the K-means clustering algorithm for device segmentation, which clusters your devices into cohorts of like devices. These templates are typically used to profile device health and device state such as HVAC units in a chocolate factory or wear and tear of blades on a wind turbine.


QuickSight Integration - AWS IoT Analytics provides a connector to Amazon QuickSight so you can visualize your data sets in a QuickSight dashboard. You can also visualize the results or your ad-hoc analysis in the embedded Jupyter Notebooks within the IoT Analytics’ console.

Learn more about AWS IoT Analytics pricing

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