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With the rapid rise of devices connected to the Internet of Things(IoT), solutions for the connection, collection, storage and analysis of the data has become imperative. AWS (Amazon Web Services) is providing services that helps connected devices in IoT to interact with cloud applications and other devices as per use cases.
Using the AWS platform can enable one to focus on the business needs without having to worry about the hassles of infrastructure management. This will enable IoT solutions to be delivered in a more effective manner.
There are volumes of data that are captured by these devices. The cloud architecture should be so aligned as to handle this information overflow. This is best done by sending the data to a queue, or buffer before storing it. Data is published to AWS kinesis or as per the AWS IoT rule can be forwarded to the AWS SQS and Kinesis for the storage. The data stored can be used to generate a custom dashboard .
Amazon Kinesis is meant for processing big data in real time. Kinesis can process hundreds of terabytes per hour from the data streaming from sources such as operating logs, financial transactions and social media feeds.
Amazon SQS (Simple Queue Service) is a message queuing service that enables you to decouple and scale micro services, distributed systems, and server less applications.
How to ensure data security with large volumes of data?. The data is redirected to an SNS, which has been designed to handle data flooding and processing, ensuring that the data is secure, managed, processed and directed properly. Data can also be stored in a Queue, Amazon Kinesis, Amazon S3, and Amazon Redshift before it is processed. This will also ensure that data is not lost due to message overflow or other implementation issues.
Data need not be stored on the cloud every time. Where net connectivity is an issue AWS Greengrass can be used on the edge. This can process and filter data on the edge, with less of the data having to be uploaded to the cloud. It allows the capturing of data on the edge for a while and the data can be sent to the cloud upon request.
The data generated from IoT devices will have different formats and scale. The data may vary in drastic levels, which may not be manageable through a single database or storage area. The database should be chosen with care. There are single database and hybrid data store to cater to the different purposes of storage. Some best practices can be followed to achieve scalability and maintainability.
The data received through IoT would needs to be processed before it is stored. The AWS platform directs the messages to its different services. An architect should be able to segmentalize data according to its status as processed, static or attention required as the case may be.
IoT architecture should ensure the integration of devices to solutions without creating any hassles. IoT capabilities do not end with the controlling, extraction and processing of data from the devices. Technologies like blockchain, Data Sciences and Machine Learning can be adopted for developing more comprehensive solutions.
Systems can achieve better operational efficiencies with least human intervention through leveraging the optimal tools that AWS provides to manage devices. Some other advantages of AWS IoT is that it helps acquire quick device connectivity, secure data processing, multiple protocol support and more.