Migration options - AWS IoT Analytics

End of support notice: On December 15, 2025, AWS will end support for AWS IoT Analytics. After December 15, 2025, you will no longer be able to access the AWS IoT Analytics console, or AWS IoT Analytics resources. For more information, see AWS IoT Analytics end of support.

Migration options

When considering a migration from AWS IoT Analytics, it’s important to understand the benefits and reasons behind this shift. The table below provides alternate options and a mapping to existing AWS IoT Analytics features.

Action AWS IoT Analytics Alternate Service Reason

Collect

AWS IoT Analytics makes it easy to ingest data directly from AWS IoT Core or other sources using the BatchPutMessage API. This integration ensures a seamless flow of data from your devices to the analytics platform.

  • Amazon Kinesis Data Streams

  • Amazon Data Firehose

Amazon Kinesis Data Streams offers a robust solution. Kinesis streams data in real-time, enabling immediate processing and analysis, which is crucial for applications needing real-time insights and anomaly detection.

Amazon Data Firehose simplifies the process of capturing and transforming streaming data before it lands in Amazon S3, automatically scaling to match your data throughput.

Process

Processing data in AWS IoT Analytics involves cleansing, filtering, transforming, and enriching it with external sources.

  • Amazon Managed Service for Apache Flink

  • Amazon Data Firehose

Amazon Managed Service for Apache Flink supports complex event processing, such as pattern matching and aggregations, which are essential for sophisticated AWS IoT Analytics scenarios.

Amazon Data Firehose handles simpler transformations and can invoke AWS Lambda functions for custom processing, providing flexibility without the complexity of Flink.

Store

AWS IoT Analytics uses a time-series data store optimized for AWS IoT data, which includes features like data retention policies and access management.

  • Amazon S3

  • Amazon Timestream

Amazon S3 offers a scalable, durable, and cost-effective storage solution. Amazon S3’s integration with other AWS services makes it an excellent choice for long-term storage and analysis of massive datasets.

Amazon Timestream is a purpose-built time series database. You can batch load data from Amazon S3.

Analyze

AWS IoT Analytics provides built-in SQL query capabilities, time-series analysis, and support for hosted Jupyter Notebooks, making it easy to perform advanced analytics and machine learning.

  • AWS Glue

  • Amazon Athena

AWS Glue simplifies the ETL process, making it easy to extract, transform, and load data, while also providing a data catalog that integrates with Athena to facilitate querying.

Amazon Athena takes this a step further by allowing you to run SQL queries directly on data stored in Amazon S3 without needing to manage any infrastructure.

Visualize

AWS IoT Analytics integrates with QuickSight, enabling the creation of rich visualizations and dashboards.

  • Amazon QuickSight

Continue to use QuickSight depending on the alternate datastore you decide to use, like Amazon S3.