Processing Large Messages Should Not Be A Problem Anymore!

To compete in this digital-first era, shifting to the cloud, adopting stream processing, and replacing obsolete technologies has become imperative nowadays. Technology executives are now largely rolling out real-time business options powered by Apache Kafka to more effectively respond to these growing needs of current and future businesses. Apache Kafka has become the main nervous system of almost every enterprise. This streaming platform enables organizations to scale quickly to improve customer experience, reduce risk and realize the benefits of digital business transformation. Even though this technology is still relatively new, it has been extensively used and is already providing enterprise-grade dependability on a broad scale. Furthermore, processing huge messages in Kafka is no longer a difficult operation, and there are a variety of use cases for high message payloads. We’ve gone over the use cases, architecture, and trade-offs for processing large messages with Kafka in this post. Take a look!

Use Cases for Kafka’s Large Message Handling

Image Recognition and Video Analytics

As the digital world becomes more visible, so too does the number of images or videos produced daily, and many of these images or videos require real-time or near-real-time processing. Kafka meets these needs and guarantees fast computation and large amounts of in-memory storage. All in all, meeting both demands in a single box can be incredibly expensive, but it will prove to be the most rewarding investment. 

Video/image processing mainly involves these three distinct tasks:

• Object Detection
• Object Recognition
• Object Tracking

Object Detection

Kafka can efficiently recognize large-scale images and video frames. Applications using Kafka object detection include facial recognition, car detection, online images, pedestrian counts, and security systems.

Object Recognition

Object recognition is a more advanced variant of object detection that starts with object detection, then maps the searched image to a known related sample dataset to match features, and tries to recognize a unique object. Face recognition, number plate identification, and handwriting recognition are all examples of object recognition.

Object Tracking

Object tracking is a somewhat more complex scenario that may be used to track the behavior of objects. Object detection is employed in tracking to create an initial input, and the identified object is tracked throughout the footage. This tracking system might be considered a difficult operation for a variety of reasons. When seen as a human, it is extremely difficult owing to perceptual features and interferences such as postures, lighting conditions, and illuminations. To track a recognized item, many object tracking techniques and approaches can be employed.

Audio/Voice Analytics

Kafka Audio Analytic is a voice recognition pioneer, allowing consumer technology companies to integrate context-based intelligence into next-generation products. Here are several Kafka usage scenarios where video and audio are typically handled together:

• Industrial IoT (IIoT) Use Case: Machine diagnostics and predictive maintenance utilizing sophisticated sound analysis, such as Neuron Soundware.
• Consumer IoT (CIoT) Use Case: Alerting, advising individuals, and related technique utilize audio analytic.
• Industrial IoT (IIoT): Advanced sound analysis used primarily in predictive maintenance and machine diagnostics, eg. Neuron soundware.
• Natural Language Processing (NLP) Use Case: Chatbots work for almost all industry verticals, ensuring to provide qualified leads and expedite customer proposals. It uses text and speech translation.

Big Data File Processing 

Big data is becoming increasingly valuable in supply chain analysis. To enhance supply chain decision-making, big supply chain analytics employs big data and quantitative techniques. Big data supply chain analytics, in particular, expands the available data sets for better analysis beyond the usual internal data found on ERP and supply chain management (SCM) systems. Furthermore, big data file processing employs very effective statistical techniques on both new and old data sources.

In A Nutshell,

Kafka’s popularity has grown over the years and has made great strides due to which processing large messages is no longer a problem. As mentioned above, image recognition, video analysis, audio analysis, and big data file processing are all common use cases for large message payloads. Overall Kafka is living up to its big promise. It is clear that Apache Kafka‘s tremendous momentum will continue, as evidenced by the growing commercial interest in applying sophisticated techniques such as machine learning to full production to process large messages. You can take it a step further by contacting big data analytics and consulting firm like Ksolves to get the big data processing done smoothly. As one of the top big data developers, the firm can help you streamline or simplify the process by connecting systems and guaranteeing continuous communication between machines and humans. By associating with Ksolves, businesses can generate long-term profitability and become industry leaders to define production and quality standards.