Step 1: Redirect ongoing data ingestion - Amazon IoT Analytics
Pattern 1: Amazon Kinesis Data Streams with Amazon Managed Service for Apache FlinkPattern 2: Use Amazon Data Firehose
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End of support notice: On December 15, 2025, Amazon will end support for Amazon IoT Analytics. After December 15, 2025, you will no longer be able to access the Amazon IoT Analytics console, or Amazon IoT Analytics resources. For more information, visit this Amazon IoT Analytics end of support.

Step 1: Redirect ongoing data ingestion

The first step in your migration is to redirect your ongoing data ingestion to a new service. We recommend two patterns based on your specific use case:

suggested architecture

Pattern 1: Amazon Kinesis Data Streams with Amazon Managed Service for Apache Flink

In this pattern, you start by publishing data to Amazon IoT Core which integrates with Amazon Kinesis Data Streams allowing you to collect, process, and analyze large bandwidth of data in real time.

Metrics and Analytics

  1. Ingest Data: Amazon IoT data is ingested into a Amazon Kinesis Data Streams in real-time. Amazon Kinesis Data Streams can handle a high throughput of data from millions of Amazon IoT devices, enabling real-time analytics and anomaly detection.

  2. Process Data: Use Amazon Managed Service for Apache Flink to process, enrich, and filter the data from the Amazon Kinesis Data Streams. Flink provides robust features for complex event processing, such as aggregations, joins, and temporal operations.

  3. Store Data: Flink outputs the processed data to Amazon S3 for storage and further analysis. This data can then be queried using Amazon Athena or integrated with other Amazon analytics services.

Use this pattern if your application involves high-bandwidth streaming data and requires advanced processing, such as pattern matching or windowing, this pattern is the best fit.

Pattern 2: Use Amazon Data Firehose

In this pattern, data is published to Amazon IoT Core, which integrates with Amazon Data Firehose, allowing you to store data directly in Amazon S3. This pattern also supports basic transformations using Amazon Lambda.

Metrics and Analytics

  1. Ingest Data: Amazon IoT data is ingested directly from your devices or Amazon IoT Core into Amazon Data Firehose.

  2. Process Data: Amazon Data Firehose performs basic transformations and processing on the data, such as format conversion and enrichment. You can enable Firehose data transformation by configuring it to invoke Amazon Lambda functions to transform the incoming source data before delivering it to destinations.

  3. Store Data: The processed data is delivered to Amazon S3 in near real-time. Amazon Data Firehose automatically scales to match the throughput of incoming data, ensuring reliable and efficient data delivery.

Use this pattern for workloads that need basic transformations and processing. In addition, Amazon Data Firehose simplifies the process by offering data buffering and dynamic partitioning capabilities for data stored in Amazon S3.