AWS as an IoT Platform
As we’ve previously discussed, IoT is a great tool to accelerate your business, particularly when it comes to product maintenance and insight. However, choosing the right IoT backend provider is vital to ensuring success in your IoT efforts. According to Crisp Research, of the many options on the market AWS’ IoT Platform offers “the most attractive IoT backend provider, profiting vastly from its strong public cloud portfolio.” The diversity and innovation of AWS provides consumers with options to develop a wide range of IoT applications, and their service offerings allow for incredible agility and flexibility when setting up IoT products for data ingestion, computation, and analytics.
Ingest with AWS
Amazon’s IoT Platform combined with the full breadth of AWS’ full portfolio of services provides customers with an advantage in terms of function and performance, allowing a variety of capabilities and options when developing applications.
AWS offers a number of capabilities for devices to communicate with applications. While bidirectional messaging exists in the AWS IoT core, special attention must be given to communications with direct publication as out-of-order messages can impact outcomes.
For example, let’s say you have a home sprinkler system you forgot to turn on. You use an application to turn it on. However, you then realize the forecast calls for rain, therefore you don’t actually need the sprinkler to be on. You turn it off. If your device receives these messages out of order, receiving the off first, when already off, then receiving the on message after, the device will wind up turning on.
To overcome the headache of these situations created by out-of-order messages, AWS IoT utilizes device shadows, which allows application logic to view a device’s last reported state and adjust that state to the desired state through a delta. Additional Amazon services, e.g. Amazon API Gateway and Amazon Cognito, also serve the ingest portion of IoT, offering additional support for application communication and security.
Compute with AWS
The serverless nature of AWS allows for high level scalability and availability. Although not “serverless” in the literal sense, the wide-ranging location of Amazon’s data centers offers a level of reliability that’s unparalleled, and available in a pay-for-use format, making your IoT efforts scalable to business needs without paying for idle resources. This idea of services, not servers, means not worrying about the limits of underlying infrastructure.
Because of the out-of-the-box high availability at lower cost, services offer great benefits to quickly launch your IoT products. Services automatically scale, are redundant and self-heal, and can be grouped into 3 categories: abstract, container, and infrastructure services. The use of primarily abstract services is ideal for serverless application in IoT, however, relying on container services is somewhat unavoidable. Perhaps the most powerful component of serverless computing with AWS is AWS Lambda. Lambda lets you run code without provisioning servers and in a containerized environment. Lambda is triggered by events and functions can interact with any other AWS service and supports Python, Node.js, Java, C#, and Go.
Analyze with AWS
In addition to more traditional server-based storage like Amazon Redshift, AWS uses data lakes as a more innovative means of storing data. A data lake is a storage repository that eschews database structure and format and stores data in the native format of the source data with a flat instead of hierarchical format. Instead of writing to schemas and query patterns, data is stored as is. AWS simplifies formations of Data Lakes with Amazon S3.
A data lake’s major distinction is the schema-on-read nature. Traditional data warehouses use schema-on-write, which has a schema applied as data is written to the database. While this may make querying easier, it is possible to lose data during the transformation due to the schema not accounting for certain precision in the raw data you may need later on. Similarly, query patterns can impact data return, particularly if a query doesn’t match the schema. In schema-on-read, differing schemas are applied when data is being read, allowing more flexibility in the data being analyzed, as different questions can be asked in these schemas.
While S3 is a powerful means of storage, a number of services integrate with S3 to simplify data analysis. This includes Amazon Kinesis to transfer data into S3, Amazon EMR to do big data processing, Amazon Athena for ad-hoc query access in real-time, and Amazon RedShift, a more traditional data warehouse used for larger long-term storage.
The AWS Value
Whether using IoT for remote control of a device, device tracking, or to collect and predict performance information, the setup of a strong and functional IoT application requires an unprecedented amount of connectivity and flexibility. AWS’ IoT services allows you to leverage a number of AWS solutions to Ingest, Compute, and Analyze for an optimized IoT experience at a fraction of the cost.