What is AWS IoT Analytics?

Posted by madhu Dm on August 26th, 2019

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.

Key Features

Collect

• Integrated with AWS IoT Core – AWS IoT Analytics is fully integrated with AWS IoT Core so it can receive messages from connected devices as they stream in.
• Use a batch API to add data from any source – AWS IoT Analytics can receive data from any source through HTTP. That means that any device or service that is connected to the internet can send data to AWS IoT Analytics. 
• Collect only the data you want to store and analyze – You can 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. AWS 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.

Process

• Cleanse and filter – AWS IoT Analytics lets you define AWS Lambda functions that are triggered when AWS 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, and then route the data to the AWS IoT Analytics data store.

Store

• Time-Series Data Store - AWS IoT Analytics stores the device data in an optimized time-series data store for faster retrieval and analysis. You can also 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.

Analyze

• Run Ad-Hoc SQL Queries - AWS IoT Analytics provides a SQL query engine so you can run adhoc queries and get results quickly. For example, you might want to run a quick query to find the number of active users 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 notebooks in Jupyter Notebooks for statistical analysis and machine learning. The service includes a set of 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. After you author a notebook, you can containerize it and execute it on a schedule you specify (Automating Your Workflow).
• Prediction - 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. Again, these notebook templates can be containerized and executed on a schedule. To get in-Depth knowledge on IoT you can enroll for live IoT Training
   

Build/Visualize

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