Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com
Last Updated: Mar 27, 2024
Difficulty: Easy

Pipelines Concept in IoT Analytics

Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

Companies must develop an open IoT architecture that adopts a holistic approach to data and analytics, allowing them to see a comprehensive picture of their whole manufacturing facility.

"You must combine data from several sources into a single holistic data platform."

"You also need an open, agnostic data pipeline that connects your devices to your platform," said Philipp Redlinger, IoT architect at System Vertrieb Alexander in Germany (SVA). "The pipeline must be able to incorporate data in various forms from numerous sources." As a result, it must be a protocol-agnostic pipeline that can be expanded to include additional protocols. In addition, the platform must be open on both the intake and output sides.

It should be able to share data and insights with end-users through desktop and mobile clients. It must also be able to communicate with other systems via a well-defined API."

"A data pipeline in combination with an open platform is vital to allow interoperability and data-driven apps and end-to-end scenarios," he added.

During last week's Splunk.conf21 event, Redlinger and his SVA colleague, IoT engineer Patrick Nieto Castro, co-presented the "From Sensor To Cloud" session.

The data pipeline of the IoT architecture, according to Redlinger, should not only be "a basic data funnel that just ingests all data" that passes through it.

"It should be able to execute data processing such as converting and filtering your data at an early stage to improve the quality of your data." You'll be able to give high-value insights for your business cases by transforming raw data into refined data that is ideally prepared for your analytical applications."

Create the IoT Analytics Pipeline

A pipeline accepts messages from a channel and allows them to be processed and filtered before being stored in a data storage. A pipeline is used to link a track to a data repository. Navigate to the AWS IoT Analytics console.

  1. Navigate to Pipelines in the left navigation window.
  2. Make a fresh pipeline:
  3. Identifier: streampipeline
  4. source of pipeline: streamchannel
  5. When you click Next, IoT Analytics will analyze the data from your channel and display the properties of your simulated device. All messages are chosen by default.

Pipeline sourcesource

  1. Select Next.
  2. You can transform the data in your pipeline and add or delete properties under 'Pipeline activities.'
  3. Choose Calculate a message attribute as the type when you click Add Activity.
  4. Cost is an attribute.
  5. (sub metering 1 + sub metering 2 + sub metering 3) * 1.5 is the formula.
  6. You may check your calculation by selecting Update preview, and the cost attribute will show in the message payload below.
  7. By choosing Add activity and Remove characteristics from a message, you may add a second activity.
  8. Click 'Next' on the id attribute. The id property is a Device Simulator-generated unique identifier. However, it adds noise to the data set.
  9. When you click Update preview, the id property in the message payload will be removed.

Pipeline Transformed

source

 

  1. Select 'Next.'
  2. The output of pipeline: Select 'iotastore' from the 'Edit' menu.
  3. Create a Pipeline by clicking the Create Pipeline button.
Get the tech career you deserve, faster!
Connect with our expert counsellors to understand how to hack your way to success
User rating 4.7/5
1:1 doubt support
95% placement record
Akash Pal
Senior Software Engineer
326% Hike After Job Bootcamp
Himanshu Gusain
Programmer Analyst
32 LPA After Job Bootcamp
After Job
Bootcamp

Frequently Asked Questions.

What are the two functions of 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 key use cases of AWS IoT Analytics?

AWS IoT Analytics also supports Time-series analyses, allowing you to analyze device performance over time, understand how and where they are used, and continuously monitor device data to predict maintenance issues and monitor sensors to anticipate and react to environmental conditions.

What is AWS IoT Analytics?

AWS IoT Analytics is a managed service that allows IoT devices to analyze advanced data. AWS IoT Analytics is a time-series data storage that will enable you to gather, research, and store IoT data. The data may then be analyzed using queries.

What is lambda in IoT?

You establish a rule to call a Lambda function when you wish to contact another AWS service or a third-party service. AWS IoT calls your Lambda function asynchronously when an incoming IoT message triggers the rule, passing data from the IoT message to the process.

What is a pipeline in IoT?

The Internet of Things data pipeline is the technology stack that controls all data as it travels from connected endpoint devices to centralized analytics or storage, including data collection, aggregation, and analysis.

Conclusion

So that's the end of the article Pipelines Concept in IoT Analytics

After reading about the Pipelines Concept in IoT Analytics, are you not feeling excited to read/explore more articles on the topic of IoT Analytics? Don't worry; Coding Ninjas has you covered.

Check out this article - Components Of IOT

Upskill yourself in Data Structures and Algorithms, Competitive Programming, JavaScript, System Design, and more with our Coding Ninjas Studio Guided Path! If you want to put your coding skills to the test, check out the mock test series and enter the contests on Coding Ninjas Studio! If you're just getting started and want to know what questions big giants like Amazon, Microsoft, and Uber ask, check the difficultiesinterview experiences, and interview bundle for placement preparations.

However, you may want to pursue our premium courses to give your job an advantage over the competition!

Please vote for our blogs if you find them valuable and exciting.

Happy studying!

Topics covered
1.
Introduction
2.
Create the IoT Analytics Pipeline
3.
Frequently Asked Questions.
3.1.
What are the two functions of AWS IoT Analytics?
3.2.
What are the key use cases of AWS IoT Analytics?
3.3.
What is AWS IoT Analytics?
3.4.
What is lambda in IoT?
3.5.
What is a pipeline in IoT?
4.
Conclusion