One of the significant highlights of the Amazon Web Services (AWS) re:Invent 2017 conference is the company’s IoT Analytics; a fully-managed service that makes the experience of running sophisticated analytics on massive volumes of IoT data flawless. The new AWS system eliminates the worry of cost and complexity typically incurred during the build and deployment of a personal IoT analytics platform. AWS IoT Analytics has rendered an effortless way to run analytics on IoT data, along with gathering ongoing insights to better the experience of decision making for IoT applications and machine learning.
The Complexities of Unstructured Data
Since IoT data is highly unstructured, it became a mission for AWS to simplify data structures so that it would become easier for cognitive computing solutions to analyze the IoT database. This idea is executed through business intelligence tools that are designed to process large unstructured data. IoT data is procured mainly through reasonably noisy processes, which in turn produces extensive and complex data with gaps, corruption, false reading and so on; this data needs to be taken care of before any analysis can occur. Besides, IoT data is often integrated into the context of other data from external sources and must be managed appropriately.
Are you utilizing analytics and the existing information provided by your system to increase problem solving and overcome the obstacles to processing big data? Amazon’s AWS IoT Analytics allows for customers to solve complex problems without complex solutions. Our team here at Idexcel is at the ready and available to work with those who want to ensure they are getting the most out of their AWS setup. Be sure to reach out for our cloud advisory services and accelerate your journey to the cloud.
Analyzing Problems and Providing Solutions
AWS IoT Analytics automates each of these problematic steps that are required to analyze data from IoT devices. IoT Analytics acts as a catalyst that filters, transforms, and enriches information before storing it in a time-series data storage for analysis. The service can then be customized according to the business: which, how much, and when to use appropriate data. AWS IoT Analytics applies mathematical equations to process and then enrich the data with device-specific metadata. Data is then analyzed by running queries using the built-in SQL query engine. IoT Analytics kick starts the process and provides better scope for outputting high accuracy information. IoT Analytics also exhibits the ability to facilitate machine learning through employing pre-built models of common IoT use cases; it can then quickly respond to probable system failure or system incompatibility and suggest replacement of hardware.
AWS IoT Analytics can keenly examine and scale automatically to support up to petabytes of IoT data; it helps analyze data from millions of devices and build fast, responsive IoT applications without managing different hardware or infrastructures. The service complements the driving forces of current IoT infrastructure with differing advancements.
It is worth noting some of the most important benefits of IoT Analytics include:
Quick and Easy Queries on Massive IoT Data – With the help of a built-in IoT Analytics SQL query engine, it becomes effortless to run ad-hoc queries; this service enables the user to use standard SQL queries to extract data directly from the data store to answer potential questions.
Time-Series Analytics – AWS IoT Analytics also supports time-series interpretations to analyze the performance of devices over time in a recurring pattern, and understand their place and manner as they are being employed. Analytics can continuously monitor device data and suggest maintenance actions as needed. The system can also observe sensors to analyze and react to environmental conditions.
Data Storage Optimized for IoT – AWS IoT Analytics stores processed device data and can deliver fast response times on IoT queries. The source data is automatically stored for later processing or to reprocess it for another use case, creating a more intelligent dataset.
Prepare IoT Data for Analysis – AWS IoT Analytics also performs data preparation that makes it easy to prepare and process your data for analysis. Integrated with AWS IoT Core, the service makes it easier to ingest device data directly from connected devices. IoT Analytics filters the data apart from corruption, false readings, and errors, and then the system performs mathematical transformations of message data. Using conditional statements the analytical service filters data, and then collects specific data required for analysis; it also gives the option of using AWS Lambda functions to enrich device data from external sources.
Tools for Machine Learning – AWS IoT Analytics is well suited for machine learning on IoT data as it has the ability hosts Jupyter notebooks. The administrator can directly connect IoT data to the notebook to build, train, and execute models right from the IoT Analytics console. Machine learning algorithms are applied to data all the more readily, which produces a health score for each device in the fleet.
Automated Scaling with Pay-As-You-Go Pricing – AWS IoT Analytics follows a pay-as-you-go service, with which one can analyze an entire fleet of connected devices without managing hardware or infrastructure. As the administrator’s needs change, they can expand or contract computation power. The data store will also automatically scale up or down, which results in the billing of only employed resources.