IoT data engineering

Navigating Top IoT Data Engineering Challenges With Solutions

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We are living in a world where every other object ne is evolving to become smarter  with every passing decade. From regulating our room temperatures to serving us our favourite coffee, devices utilize our data to make magic happen in our lives. Credits to data engineering IoT (Internet of Things) that is under constant development to make our lives better.

International Data Corporation (IDC) predicts that there will be 41.6 billion IoT devices in 2025, qualified for developing 79.4 zettabytes (ZB) of data. Sad to say, but every coin has two signs.  Certain challenges are being faced by IoT that require smart solutions. Let’s proceed to see what they are. 

How Does IoT Data Engineering Work?

The Internet of Things consists of interconnected smart devices that collect, transfer, exchange; and transmit data without human involvement. On the other hand, data engineering ensures the perfect data process; it involves building systems to store and collect data for smart devices.

Below mentioned five stages will explain how IoT works:

  1. Data Ingestion is the first step, which involves deciding the mechanism for collecting data from wired or wireless sources.
  2. In the next step, data engineers work to manage data storage in IoT devices. Different formats of data in huge quantity must be stored systematically.
  1. The stored data can’t be directly used, and therefore, the next step is to transform the data. It includes cleaning, organizing, and arranging the data.
  1. In the fourth stage, data processing begins, where data is analyzed, and visualized using data visualization tools. With modern techniques, a feature of real-time processing is possible.
  1. Last comes the concept of data security. In this stage, the data collected needs to be secured with encryption to eliminate unauthorized access and make it useful in the future.

The whole process requires the expertise of data engineers, and that is why you will notice the job title of data engineer climbing the ladder of a data science career. 

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Challenges Faced  by Data Engineering for IoT and Ways to Overcome

Whether small, medium, or large, organizations face a few challenges in data engineering for IoT.

  1. Wide Data Scope

Collecting huge amounts of data for processing purposes brings data workload for the system. This data needs to be systematically processed to extract insights. It is critical to identify which data set is useful and which is not, or else professionals in data science career will get trapped in the loop of data.

The solution to this problem is to introduce scalable infrastructure. Platforms like Google Cloud and AWS can be scaled according to demand. IoT data can be stored and processed easily on these platforms. 

  1. Data Security

Forbes states that in 2025, as many as 152,200 IoT devices will connect to the Internet every minute. it increasing software hacking, IoT devices are vulnerable to the risk of being targeted for precious user data.

Data engineers are responsible for introducing security measures like complete data encryption and other protocols to protect data. Depending upon a reliable VPN can be highly effective, as all the other connected devices will follow the same security standard.

  1. Real-Time Data Processing

Implementing real-time data processing in data engineering IoT devices is challenging as there is a continuous flow of data in all the connected devices. To conduct data processing in a few milliseconds, strong bandwidth and storage resources are required to avoid straining the infrastructure.

Introduction of tools that incorporate parallel processing i.e processing the data as soon as it arrives, is crucial for organizations. Apache Storm & Apache Kafka, tools and frameworks like this can aid organizations.

  1. Data Integration

The primary goal of IoT Data Engineering is to connect with different data sources and integrate complete data in a single suitable view. However, IoT devices have different formats and processes, making data integration complicated.

For seamless operations, data science professionals must introduce data integration, data pipelines, and IoT devices that have open protocols like OPC UA, CoAP, etc. Further comprehensive analysis can be enabled with data mapping and data visualization tools.

These solutions can overcome the repetitive challenges on IoT data engineering., enabling organizations to ensure the continued functionality of IoT systems.

Closing Remarks

As we saw the challenges of IoT data engineering applications, it is crystal clear that organizations can overcome them by applying progressive analytics, dedicated data pipelines, and scalable storage facilities and enjoying the applications. By openly encouraging data transparency and security, businesses can harness the advantages of advanced technologies.

With this, let us continue to explore and innovate IoT technology to stay more connected with smart devices and let technology make our lives easier.

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