AWS IoT Analytics is a fully-managed service that makes it easy to run and operationalize sophisticated analytics on massive volumes of IoT data without having to worry about the cost and complexity typically required to build an IoT analytics platform. It is the easiest way to run analytics on IoT data and get insights to make better and more accurate decisions for IoT applications and machine learning use cases.
IoT data is highly unstructured which makes it difficult to analyze with traditional analytics and business intelligence tools that are designed to process structured data. IoT data comes from devices that often record fairly noisy processes (such as temperature, motion, or sound). The data from these devices can frequently 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 additional, third party data inputs. For example, to help farmers determine when to water their crops, vineyard irrigation systems often enrich moisture sensor data with rainfall data from the vineyard, allowing for more efficient water usage while maximizing harvest yield.
AWS IoT Analytics automates each of the difficult steps that are required to analyze data from IoT devices. AWS IoT Analytics filters, transforms, and enriches IoT data before storing it in a time-series data store for analysis. You can setup 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 the processed data. Then, you can analyze your data by running ad hoc or scheduled queries using the built-in SQL query engine, or perform more complex analytics and machine learning inference. AWS IoT Analytics makes it easy to get started with machine learning by including pre-built models for common IoT use cases.
You can also use your own custom analysis, packaged in a container, to execute on AWS IoT Analytics. AWS IoT Analytics automates the execution of your custom analyses created in Jupyter Notebook or your own tools (such as Matlab, Octave, etc.) to be executed on your schedule.
AWS IoT Analytics is a fully managed service that operationalizes analyses and scales automatically to support up to petabytes of IoT data. With AWS IoT Analytics, you can analyze data from millions of devices and build fast, responsive IoT applications without managing hardware or infrastructure.
The IoT Analytics Platform module focuses on delivering measurable business value, using intelligent big data processing and real-time data analytics for M2M/IoT business purposes. It provides thorough information about the way in which customers are using your IoT products. Additionally, it handles some issues related to quality of service (QoS) within operations, by helping discover which device (or type of device) generates issues, revealing data patterns and trends, and providing reports and analyses of anomalies. Device/IMSI (International Mobile Subscriber Identity) or customer profile dashboards enable deep data drill down. You can also use the supporting functions such as alarms and notifications internally, to improve your business and operational results and enhance customer experience using this solution, which enables you to truly unlock the potential of IoT (Internet of Things) analytics data for telecom organizations
AWS IoT Analytics is ranked 2nd in IoT Analytics while Comarch IoT Analytics Platform is ranked 10th in IoT Analytics. AWS IoT Analytics is rated 0.0, while Comarch IoT Analytics Platform is rated 0.0. On the other hand, AWS IoT Analytics is most compared with ThingSpeak, Altair SmartWorks and Google Cloud IoT Core, whereas Comarch IoT Analytics Platform is most compared with .
See our list of best IoT Analytics vendors.
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