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.
Cumulocity IoT DataHub allows you to connect existing analytics tools and applications to Cumulocity IoT, such as: Business Intelligence and machine learning tools, as well as arbitrary custom applications, and much more. Enable your users to understand why certain events occur, whether these are normal, whether they are likely to happen again, and what was the end result previously, whether positive or negative.
Cumulocity IoT DataHub offers you SQL-based access via ODBC/JDBC and REST to your IoT data. Store the large volume of valuable information from sensors and devices for much longer than the usual 14 day period in a much more cost-effective way than previously possible in your data lake. Access all time-series data from your preferred tools and applications. Enable your teams to compare historical data from the same device types across multiple locations, explore, analyze, and act on this data.
Use your data efficiently. Understand the past to predict the future and action sophisticated data-driven outcomes.
Learn more about Cumulocity DataHub at Data Lake for IoT Analytics | Cumulocity IoT DataHub (softwareag.com) and more on our IoT Platform at IoT Platform | Cumulocity IoT | Software AG
AWS IoT Analytics is ranked 2nd in IoT Analytics while Cumulocity IoT DataHub is ranked 6th in IoT Analytics. AWS IoT Analytics is rated 0.0, while Cumulocity IoT DataHub is rated 0.0. On the other hand, AWS IoT Analytics is most compared with ThingSpeak, Altair SmartWorks and Google Cloud IoT Core, whereas Cumulocity IoT DataHub is most compared with .
See our list of best IoT Analytics vendors.
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