AWS IoT Analytics Documentation
AWS IoT Analytics helps you run and operationalize analytics on IoT data. AWS IoT Analytics helps you with the difficult steps that are required to analyze data from IoT devices. AWS IoT Analytics is designed to accept data from sources including Amazon Kinesis, Amazon S3, or third party tools, using an API and is 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 is designed to perform data transformations, execute conditional statements, and enrich messages with data from external sources.
After processing the data, AWS IoT Analytics is designed to store 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.
AWS IoT Analytics is designed to ingest data directly from AWS IoT Core. Or, use an API to send your data to AWS IoT Analytics from Amazon S3, Amazon Kinesis or other sources. With AWS IoT Analytics' integration with AWS IoT Core and the API, it is easy to receive messages from connected devices as they stream in.
The AWS IoT Analytics console is designed so you can configure AWS IoT Analytics to receive messages from devices through MQTT topic filters in various formats and frequencies. AWS IoT Analytics helps validate that the data is within specific parameters you define and creates channels. Then the service is designed to route the channels to appropriate pipelines for message processing, transformation, and enrichment.
AWS IoT Analytics let you define AWS Lambda functions that can help serve as triggers on when AWS IoT Analytics detects missing data, so you can run code to estimate and fill gaps. You can also define filters and thresholds to remove outliers in your data.
AWS IoT Analytics is designed to transform messages using mathematical or conditional logic you define, so you can perform common calculations like Celsius into Fahrenheit conversion.
AWS IoT Analytics can help enrich data with external data sources such as a weather forecast information, and then route the data to the AWS IoT Analytics data store.
AWS IoT Analytics is designed so you can reprocess raw data from the Channel connected to the Pipeline. Reprocessing your raw data can give 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 helpful to gain deeper insights or test hypothesis. Simply connect the Pipeline to the appropriate Channel to reprocess.
AWS IoT Analytics is designed to store the device data in an IoT optimized time-series data store for analysis, and allow you to manage access permissions, implement data retention policies and export your data to external access points.
AWS IoT Analytics is designed to store the processed data and also the raw ingested data so you can process it at a later time.
AWS IoT Analytics is designed to provide 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.
AWS IoT Analytics is designed to support 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.
AWS IoT Analytics is also designed to include support for hosted 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 certain IoT use cases.
AWS IoT Analytics can help you import your custom authored code containers, built in AWS IoT Analytics or a third party, such as Matlab, or Octave, etc., giving you more time to focus on what sets you apart from your competition.
If you are using Jupyter Notebooks, AWS IoT Analytics is designed to allow you to create an executable container image of your Jupyter Notebook code and visualize your container analysis on the AWS IoT Analytics console.
AWS IoT Analytics is also designed to help you with the execution of containers hosting custom authored analytical code or Jupyter Notebooks to perform analysis, including by scheduling execution of your custom analysis on a recurring schedule that best meets the need of your business.
AWS IoT Analytics is designed to enable users to perform analysis on new incremental data captured since the last analysis, helping you to improve analysis efficiency and lower costs by precisely scanning just your new data.
AWS IoT Analytics is designed with a connector to Amazon QuickSight so you can visualize your data sets in a QuickSight dashboard. AWS IoT Analytics is also designed so you can visualize the results or your ad-hoc analysis in the embedded Jupyter Notebooks within the AWS IoT Analytics’ console.
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