Building Real-Time Data Pipelines with Edge Computing and IoT



Wissen Team


April 24, 2024

The rising quantity of IoT devices has no doubt contributed to massive volumes of generated data. The recent surge in AI and IoT adoption means that every connected device including cars, security devices, and wearables will generate vast data volumes. This data can no longer be processed using “traditional” data pipelines. Edge Computing is ready to enable this important transition.

The Edge Computing technology is now enabling real-time data pipelines, which makes it feasible to process these massive data closer to their source. Thanks to Edge Computing, there’s a lesser need to exchange data between centralized servers and connected IoT devices.

Going ahead, by the year 2030, there will be an estimated 29 billion IoT devices around the world. Enabled by Edge Computing and IoT technology, real-time data pipelines can deliver benefits like:

  • Higher bandwidth
  • Lower latency
  • Better system resilience
  • Lower operational costs
  • Better data privacy

Having said that, organizations will have their share of challenges in building real-time data pipelines. Let’s discuss them along with the solution.

  1. Real-time data processing

As more connected IoT devices generate data, there's a need for real-time, low-latency data processing to generate insights on time. The challenge is most IoT-generated data is either unstructured or semi-structured, thus complicating real-time processing. In addition to faster data processing, organizations also have to ensure data security, privacy, and regulatory compliance.

To counter this challenge, organizations need a solution that can easily handle the variety and volume of the generated IoT data, while delivering valuable insights for decision-making purposes.

  1. Lack of data synchronization

Edge Computing is now the “de-facto” technology for enabling real-time data processing at the edge of networks. However, organizations also face synchronization challenges in the form of high latency, limited bandwidth, and lack of continuous connectivity. These challenges can substantially hinder data synchronization between the edge devices and the centralized system.

The effective solution is to synchronize the edge devices with the cloud servers. Through real-time synchronization, edge devices can exchange data seamlessly with the servers. To facilitate real-time synchronization, the MQTT (Message Queuing Telemetry Transport) protocol enables low-latency lightweight communication using a publish-subscribe model.

  1. Complex hardware requirements

Complex hardware requirements are among other challenges to implementing real-time data pipelines using Edge Computing. For instance, IoT-enabled cameras need a local computer to transmit raw video data to the centralized server. Similarly, to execute motion detection, they need a more sophisticated system and processing power.

Complex tasks like data analytics are often executed on heterogeneous hardware systems and at varying distances from the data sources. To resolve these challenges, organizations can deploy a serverless cloud computing model, where tasks (in the form of virtual functions) can be migrated seamlessly between the Cloud and the Edge device.

  1. Massive data volumes

As more devices connect to the IoT network, there’s a sudden surge in data volumes. Organizations struggle to handle data of various volume, speed, and variety. Traditional data processing techniques are not equipped to handle this level of complex data.

Organizations need innovations like Edge Computing to handle complex IoT data and extract insights on time.

  1. Lack of ownership

Among the common challenges in implementing Edge technology, organizations do not have clear assignment of ownership rights among project stakeholders. For a successful implementation, they need clear ownership and collaboration among stakeholders across the following departments:

  • Information technology (IT) team for managing the Edge Computing and IoT technology tools.
  • Communication technology team for managing the real-time processing and transmission of information.
  • Operational technology team for managing the client hardware and software solutions.

Best Practices for Building Real-time Data Pipelines

Despite their relevance, organizations find it challenging to build real-time data pipelines. Here are some best practices that can enable them:


Before building a real-time data pipeline, organizations must clearly define their goals in adopting this technology. This includes complete clarity over the:

  • The business problem to tackle.
  • The data sources feed into the pipeline.
  • The metrics and KPIs to track the performance of the data pipeline.

  • Perform real-time data integration

Through real-time data integration, organizations can ensure the continuous flow and collection of real-time IoT data. They cannot ensure real-time data using data collection through batch processing. Adopt the right Edge Computing tool to ensure the latest data.

  • Select the right tools and technologies

Another best practice is to select the right tools and technologies to build the data pipeline depending on the data volumes, variety, and speed. This includes using tools like Kafka or Apache Flink for real-time or event-driven data processing. Similarly, serverless data pipelines can deliver business value for IoT-powered data processing.

  • Adopt a modular approach

Enterprises often err by adopting a monolithic approach to building a data pipeline. This makes it complicated for them to troubleshoot problems and performance-related issues. A modular architecture is a better approach for building real-time data pipelines that are easy to scale. Similarly, they can implement microservices to manage individual data pipeline components.


Organizations that require real-time data pipelines to process IoT data need to transition from the “traditional” approach to the latest approach using Edge Computing. This technology is best suited for the demands of real-time processing of complex and high-volume data.

At Wissen, we believe that it’s time for our customers to leverage both cloud and Edge Computing to extract real-time data insights. With an experienced technology partner like us, you can leverage the power of both Edge Computing and IoT technology. 

If you are looking for an experienced cloud solution partner, get in touch with us now.

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