AWS IoT Analytics

How to use AWS IoT Analytics?

The workflow of AWS IoT Analytics is shown in the below diagram.

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Let’s discuss the Key Features of AWS IoT Analytics shown in the above diagram.

Key Features

Collect

1.) AWS IoT Analytics is fully linked with AWS IoT Core, allowing it to receive messages from connected devices as they are received.

2.) AWS IoT Analytics can receive data from any source through HTTP, thus using a batch API to add data from any source. AWS IoT Analytics can receive data from any device or service connected to the internet.

3.) Collect only the information you intend to store and analyze.

Process

1.) Cleanse and Filter - AWS IoT Analytics allows you to create AWS Lambda functions triggered when missing data is detected, allowing you to run code to estimate and fill gaps.

2.) Transform - AWS IoT Analytics can modify signals using the mathematical or conditional logic you provide.

3.) Enrich - AWS IoT Analytics can enrich data with external data sources like weather forecasts before sending it to the AWS IoT Analytics data store.

Store

1.) Time-Series Data Store - AWS IoT Analytics saves device data in an optimized time-series data store for faster retrieval and analysis. 

2.) Store Processed and Raw Data - AWS IoT Analytics archives processed data and automatically stores raw ingested data so you may process it later.

Analyze

1.) Execute ad-hoc SQL queries - AWS IoT Analytics includes a SQL query engine that allows you to run ad-hoc queries efficiently.

2.) Time-series analysis - Time-series analysis is supported by AWS IoT Analytics, allowing you to examine device performance over time and learn how and where they are used.

3.) Hosted notebooks for sophisticated analytics and machine learning - Support for hosted notebooks in Jupyter Notebook for statistical analysis and machine learning are included in AWS IoT Analytics. The service comes with notebook templates that incorporate machine learning models and visualizations created by AWS.

4.) Prediction - A method known as logistic regression can be used to do statistical classification. You can also utilize LSTM(Long-Short-Term Memory), a sophisticated neural network technique for forecasting the output or state of a process that changes over time.

Build and Visualize

1.) Amazon QuickSight integration - You can visualize your data sets in a QuickSight dashboard using AWS IoT Analytics' connector to Amazon QuickSight.

2.) Console integration - You may also use the inbuilt Jupyter Notebook in the AWS IoT Analytics console to visualize your ad-hoc analysis results.

AWS IoT Analytics Components

The main components of AWS IoT Analytics are discussed below.

Channel

Before publishing data to a pipeline, a channel takes data from an MQTT topic and archives the raw, unprocessed messages. The BatchPutMessage API can also be used to deliver messages straight to a channel.

Pipeline

A pipeline accepts messages from a channel and allows them to be processed before being stored in a data storage. In the processing phases, the activities (Pipeline activities) alter your messages by removing, renaming, or adding message attributes.

Data Store

A data store is where pipelines save their processed messages. A data store is a scalable and queryable repository of your communications, not a database. You can have numerous data stores for communications originating from various devices or locations.

Data Set

A data set is used to retrieve data from a data repository. You can generate a SQL data set or a container data set with AWS IoT Analytics.

SQL Dataset

A materialized view from a SQL database is similar to a SQL data set. A SQL data set can be created by using a SQL action. By defining a trigger, SQL data sets can be created automatically on a recurrent basis.

Container Dataset

A container data collection allows you to automatically run your analytic tools and generate findings. It takes as input a SQL data set, a Docker container, and your analysis tools.

Trigger

A trigger can be used to create a data set automatically. A time interval or when the content of another data set is created can be used as a trigger. You can also use the CreateDatasetContent API to generate data set content manually.

Docker Container

You can utilize SageMaker's tools or develop your own Docker container to bundle your analytic tools.

Delta Windows

Delta windows are a set of non-overlapping, contiguous time intervals defined by the user. Delta windows allow you to create data set content with new data that has arrived in the data store since the last analysis and perform analysis on it.

Automating The Workflow

For AWS IoT, AWS IoT Analytics provides enhanced data analysis. Using data analysis and machine-learning tools, you may automatically collect IoT data, process it, store it, and evaluate it. To avoid replicating existing analytical tools, you can utilize third-party custom code containers or execute containers that hold your custom analytical code or Jupyter Notebook.

To feed data from a data store into an automated workflow, you can use the following capabilities:-

Create dataset content on a recurring schedule

When you run CreateDataset, specify a trigger to automate the creation of dataset content (triggers:schedule: expression). The dataset content is created using data from data storage.

Define a non-overlapping, contiguous time interval to ensure that the new dataset only contains data that has arrived since the last time. Use the following actions: To provide the delta time interval, use the queryAction:filters:deltaTime and :offsetSeconds fields. Then, when the time interval has passed, define a trigger to construct the dataset content.

Create dataset content upon completion of another dataset

Trigger creation of new dataset content when another dataset's content creation is complete triggers:dataset:name.

Automatically run your analysis applications

Create your own custom data analysis programs and have them execute when the content of another dataset is created. This allows you to feed data from a dataset's content created periodically into your application. You can automatically take action on your analysis results from within your application. (actions:containerAction)

Automating UseCases

Some of the major automated use cases are given below.

Predictive Maintenance

AWS IoT Analytics provides templates for creating and applying predictive maintenance models to your devices. For example, you can use AWS IoT Analytics to forecast when connected cargo vehicles' heating and cooling systems will likely fail, allowing the vehicles to be diverted to avoid shipment damage.

Proactive Replenishing of Supplies

AWS IoT Analytics enables you to create IoT apps that track inventory in real-time. A food and beverage corporation, for example, can examine data from food vending machines and proactively replenish items when supplies run low.

Process Efficiency Scoring

You may use AWS IoT Analytics to create IoT apps that continuously assess the efficiency of various processes and take action to enhance them. A mining corporation, for example, can improve the efficiency of its ore trucks by increasing the load per trip. The organization may use AWS IoT Analytics to determine the most efficient load for a location or vehicle over time, compare any variations from the goal load in real-time, and better design leading guidelines to increase efficiency.

Smart Agriculture

AWS IoT Analytics can supplement IoT device data with contextual metadata, such as time, location, temperature, altitude, and other environmental conditions, utilizing AWS IoT registry data or public data sources. Based on your findings, you can create models that suggest actions for your devices to take in the field. For example, irrigation systems might combine humidity sensor data with rainfall data to determine when to irrigate, allowing for more effective water use.

Security In AWS IoT Analytics

At AWS, cloud security is a principal focus. As an AWS customer, you have access to a data center and network architecture designed to fulfill the needs of the most security-conscious organizations.

AWS and you both share responsibility for security. This is referred to as Security in the cloud and Security in the Cloud under the shared responsibility model:-

1.) Security of the Cloud: AWS is in charge of safeguarding the infrastructure that powers AWS Cloud services. AWS also supplies you with services that are safe to utilize. As part of the AWS compliance initiatives, third-party auditors test and verify the effectiveness of our security.

2.) Security in the Cloud: The AWS service you utilize determines your obligation. Other considerations include the sensitivity of your data, the requirements of your company, and applicable laws and regulations.

Check out this article - Components Of IOT

Frequently asked questions

What is IoT?

The Internet of Things (IoT) is a term that refers to a collection of devices or objects that are connected via the Internet. The collection hub (the "things" part) is what sends and gathers data over the Internet, allowing devices to make judgments and recall specific patterns and routines so that actions can be carried out without the need for human intervention.

What is AWS IoT Analytics?

AWS IoT Analytics streamlines the process of analyzing data from IoT devices. AWS IoT Analytics filters, converts, and enhances it before saving IoT data in a time-series data repository for analysis. 

What are the components of AWS IoT Analytics?

The main components of AWS IoT Analytics are Channel, Pipeline, Data Store, Data Set, Trigger, Delta Windows, etc.

Conclusion

In this article, we have extensively discussed AWS IoT Analytics, how to use AWS IoT Analytics, its key features, and security. If you want to learn more, check out our articles on Amazon Workmail, IoT With The Cloud, IAM Security Standard, Data Warehousing Tools, The Data Governance Challenge, Data Virtualization Use Cases, and Encapsulation. You can also consider our Data Analytics Course to give your career an edge over others.

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