AWS IoT Analytics automates the steps required to analyze data from IoT devices. It filters, transforms, and enriches IoT data before storing it in a time-series data store for analysis. You can set up the service to collect only the data you need from your devices, apply mathematical transforms to process the data, and enrich the data with device-specific metadata such as device type and location before storing it. Then, you can analyze your data by running queries using the built-in SQL query engine, or perform more complex analytics and machine learning inference. AWS IoT Analytics enables advanced data exploration through integration with Jupyter Notebooks. It also enables data visualization through integration with Amazon QuickSight.

Traditional analytics and business intelligence tools are designed to process structured data. IoT data often comes from devices that record noisy processes (such as temperature, motion, or sound). As a result the data from these devices can have significant gaps, corrupted messages, and false readings that must be cleaned up before analysis can occur. Also, IoT data is often only meaningful in the context of other data from external sources. AWS IoT Analytics enables you to address these issues and collect large amounts of device data, process messages, and store them. You can then query the data and run sophisticated analytics on it. AWS IoT Analytics includes pre-built models for common IoT use cases so you can answer questions like which devices are about to fail or which customers are at risk of abandoning their wearable devices. Take your career to new heights of success with a AWS Online Training.

Why Use AWS IoT Analytics?

Benefits

1. Run Queries on IoT Data:- With AWS IoT Analytics, you can run simple, ad-hoc queries using the built-in AWS IoT Analytics SQL query engine. The service allows you to use standard SQL queries to extract data from the data store to answer questions like the average distance traveled for a fleet of connected vehicles or how many doors in a smart building are locked after 7pm. These queries can be re-used even if connected devices, fleet size, and analytic requirements change.
2. Run Time-Series Analytics:- AWS IoT Analytics also supports time-series analyses so you can analyze the performance of devices over time and understand how and where they are being used. It allows you to continuously monitor device data to predict maintenance issues, and monitor sensors to predict and react to environmental conditions.
3. Data Storage Optimized for IoT:- AWS IoT Analytics stores the processed device data in a time-series data store that is optimized to deliver the fast response times typically needed by IoT queries. The raw data is also automatically stored for later processing or to reprocess it for another use case.
4. Prepares Your IoT Data for Analysis:- AWS IoT Analytics includes data preparation techniques that allow you to prepare and process your data for analysis. AWS IoT Analytics is integrated with AWS IoT Core so it is easy to ingest device data directly from connected devices. It can clean false readings, fill gaps in the data, and perform mathematical transformations of message data. As the data is ingested, AWS IoT Analytics can process it using conditional statements, filter data to collect just the data you want to analyze, and enrich it with information from the AWS IoT Registry. You can also use AWS Lambda functions to enrich your device data from external sources like the Weather Service, HERE Maps, Salesforce, or Amazon DynamoDB.
5. Tools for Machine Learning:- AWS IoT Analytics allows you to apply machine learning to your IoT data with hosted Jupyter Notebooks. You can directly connect your IoT data to the notebook and build, train, and execute models right from the AWS IoT Analytics console without having to manage any of the underlying infrastructure. Using AWS IoT Analytics, you can apply machine learning algorithms to your device data to produce a health score for each device in your fleet. After you author a notebook, you can containerize it and execute it on a schedule you specify (Automating Your Workflow). 

Use Cases

1. Predictive Maintenance:- AWS IoT Analytics provides templates to build predictive maintenance models and apply them to your devices. For example, you can use AWS IoT Analytics to predict when heating and cooling systems are likely to fail on connected cargo vehicles so the vehicles can be rerouted to prevent shipment damage. Or, an auto manufacturer can detect which of its customers have worn brake pads and alert them to seek maintenance for their vehicles.
2. Proactive Replenishing of Supplies:- AWS IoT Analytics lets you build IoT applications that can monitor inventories in real time. For example, a food and drink company can analyze data from food vending machines and proactively reorder merchandise whenever the supply is running low.
3. Process Efficiency Scoring:- With AWS IoT Analytics, you can build applications that constantly monitor the efficiency of different processes and take action to improve the process. For example, a mining company can increase the efficiency of its ore trucks by maximizing the load for each trip. With AWS IoT Analytics, the company can identify the most efficient load for a location or truck over time, and then compare any deviations from the target load in real time, and better plan loading guidelines to improve efficiency. To get in-Depth knowledge on IoT you can enroll for live IoT Training
4. Smart Agriculture:- AWS IoT Analytics can enrich IoT device data with contextual metadata using AWS IoT Registry data or public data sources so that your analytis factors in time, location, temperature, altitude, and other environmental conditions. With that analysis, you can write models that output recommended actions for your devices to take in the field. For example, to determine when to water, irrigation systems might enrich humidity sensor data with data on rainfall, allowing more efficient water usage.