5 challenges that developers face when building a streaming platform

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This article features a list of five primary challenges that developers face when building a streaming platform. The better the engineers can handle these issues, the more productive and user-friendly their platform will be.

When building a live streaming platform, most developers face the following challenges: selecting optimal data formats and development frameworks, carrying out algorithm testing, improving network connectivity, providing security and creating a favorable environment for scaling in performance. From this article, you’ll get to know the essence of all these challenges, which should help you to efficiently cope with them.

Selecting Optimal Data Formats and Development Frameworks

A streaming platform resembles a large data platform to a great extent. Streaming data usually concentrates in cloud-based or on-premise repositories. Domain experts and data analysts use these repositories to get insights, explore trends and design algorithms. The results of the analysis might significantly tell on how the target audience will adopt and use the final product.

Streaming data is usually assembled from a sequence of data packets, each of which carries a time stamp. To process time-series data, specialists of a web and mobile app development company need to use instruments with native data types. It allows professionals to cleanse and visualize the data and investigate its patterns. These instruments need to provide a development framework that was built specifically for the micro-batch/windowed processing that is crucial for stream processing. That should minimize the risk of errors and boost the productivity of the whole system.

Algorithm Testing, Validation, Deployment and Life-Cycle Management

To carry out algorithm testing and validation, the platform should be able to replay archived streaming data. It might be reasonable to try testing and validation not on the production streaming system but on its simplified replica. This twin needs to maximize the debugging capabilities that the algorithm development environment includes. It needs to consider the system’s capability to set breakpoints, track variables and comprehend the peculiarities of the algorithm’s functioning.

While the algorithms are still being developed, engineers should be able to assess their precision and effectiveness. Plus, they should be able to manage evaluation results and the data that the developers relied on to design the algorithm.

When the algorithms are already in operation, engineers should be able to manage their versions and juxtapose their performance to the baseline indicators that were recorded during the development stage. That should enable the development team to understand whether they need to update or fine-tune the algorithm. Otherwise, they might want to retire and replace the algorithm to make sure the product functions as intended.

Improving Network Connectivity

Some packets might fail to arrive in order or might end up being dropped. The developer has to decide how the system will cope with such packets. Also, the developers should keep in mind that the system sometimes might be disconnected from the terminal devices. Professionals should opt for a platform that can shuffle and reorder packets before they are analyzed.

To carry out feature extraction, time- and frequency-domain analysis, as well as signal processing, come in handy. An essential prerequisite for using them consists in the following: the order of data packets should be based on the time when the initial event occurred compared with the time when the streaming system received the packet. When creating the processing environment, the developer should make it capable of cleaning up time-series data with the help of smoothing, interpolating and retiming methods.

The platform should support the deployment of algorithms to a wide array of systems that include cloud, on-premise and terminal devices. Thanks to it, portions of the processing will be localized to terminal devices with intermittent connectivity. If the bandwidth between the terminal devices and the centralized stream processing infrastructure is not sufficient, such an approach should enable data reduction.


A streaming platform needs to integrate with security layers that already exist within the company. Based on their roles, users will be able to distribute and adjust access rights to data, systems and terminal devices. Besides, the platform needs to support the encryption of data, both in motion and at rest. The intellectual property of the algorithms should also be encrypted.

Scaling and Enhancing Productivity

Real-time streaming systems should react to incoming data within certain time periods. As soon as one more device goes online, the system needs to adapt to it. Streaming infrastructure normally involves partitioning and the system needs to scale accordingly. New stream processing contexts need to appear as soon as new streams or topics come up. The developer should find opportunities to increase the elasticity of the cloud and container technologies.

Final Thoughts

Hopefully, this information came in handy and now you have a better vision of the challenges that you might come across when building a streaming platform. After it is already launched and users begin to carry out streams through it, it would be wise to consistently gather analytics about it. These insights will enable you to detect bugs even before they take place and prevent them.