Harnessing the Power of Real-Time Data Processing

To keep up with the demands of today’s fast-paced digital world, businesses need real-time data that can be processed quickly and accurately.  

Imagine an e-commerce site servicing thousands of customers during a Black Friday sale or a bank simultaneously handling online and mobile app transactions every minute. The capacity to process reliable data in real time enables these companies to meet customer needs and maintain efficient operations.  

In fact, a recent survey showed that 51% of IT leaders use data streaming as a top tech strategy to achieve bigger returns on investment (ROI) for their businesses. Data streaming’s agility enabled them to access currently logged data and use them for various purposes. It also helped them gather relevant analytics to make improvements on the fly. 

Such fast turnaround times can help them cater to issues that may arise on a user or customer level, allowing them to be more reliable and competitive in the evolving digital era. 

What is Real-time Data Processing? 

Generally speaking, this technology enables engineers to architect multiple systems that analyze and act on information immediately upon receiving it. Due to its responsive nature, a series of user events may trigger actions that can create trends or generate insights for analytics.  

Have you ever noticed how pop-ups appear when you visit a website or as you click on something on a web page? Whether it’s a chatbot saying, “Hi, how may I help you?” or a discount voucher that must be used within a “limited time only,” these actions showcase how this type of processing works. It is ideal for engagements requiring instant results and could be very efficient for cross-platform app development. 

Is it Different from Traditional Batch Processing? 

Traditional batch processing relies on a bulk of information stored for a while and processes it on a predetermined schedule. In real-world applications, it enables recurring activities such as periodic subscriptions, monthly payrolls, and tracking inventories.  

Though less flexible, this type of technology is still being used as an enterprise data management system. It’s still preferred because it’s cost-effective for bulk engineering and may not need that much custom software development. However, its event responsiveness is limited. Its workflow tends to be linear and may even need to be hard coded. 

Meanwhile, real-time data processing can accommodate any event occurrences. It also gives engineers more room for improvements and changes depending on evolving use cases. 

Complex Event Processing vs Stream Processing 

Although both of them depend on user events, complex event processing (CEP) and stream processing manage and analyze real-time data differently. 

Complex Event Processing 

This method focuses on analyzing streams of events and identifying data points. It can detect patterns, determine trends, and identify relationships between events. It can also gather insights from analyzed data points and identify relationships or possible dependencies between them. 

CEP generates a wealth of information that makes it popular in doing analytic work. For example, a software development company can use it to develop pattern recognition features for cybersecurity products and services. It can also be used for temporal analysis in time-based events to generate trends.  

To date, this method has many practical and complex applications. It’s the force behind powering smart cities, enabling devices to work together as traffic management systems and in public safety monitoring. It can also help in agricultural endeavors, where farmers can easily supervise the monitoring of crop conditions amidst changing environmental conditions. Through it, businesses can improve resource management and autonomously control resource planning. 

This ability to work through a network of event sources makes CEP the preferred method for delivering IoT functions, where sensors and machine-to-machine connections are vital data points. These days, AI and other machine learning initiatives have also started to explore its usage. 

Stream Processing 

In this method, when an external input happens, a set of conditions filters through the list of actions to obtain a correct response. This method allows applications to analyze information upon arrival and respond to it, both immediately and accordingly, in a seemingly independent manner.  

Stream processing is ideal for enterprise mobile application development because it offers high throughput and low latency. With its event-driven architecture, stream processing can easily integrate with event-based systems to produce instant updates and notifications. Popular use cases for stream processing include online pop-ups, financial trading, and the prevention of fraudulent activities. In these scenarios, users need immediate results, and trends are not necessarily created per action.  

Enterprise product engineering services can benefit from this method when dealing with high-velocity data streams that require constant monitoring. Time-critical functions such as fraud detection and network monitoring can utilize its low latency capabilities. Since this type of method relies on cloud-based information, it can help simplify system infrastructure as it pulls information from a centralized location.  

Can the Two Methods Be Combined? 

The short answer is yes. In fact, in some cases, it may be ideal to combine both methods to facilitate a more natural flow of user experience. They can also be architected as complementary to each other to create a responsive system that adapts to unpredictable events. 

In a sense, stream processing is a simpler version of CEP. Their combination could foster an environment in which trial-and-error becomes beneficial to learning which systems work and which ones to scrap. A systems architecture that combines both methods can inspire business productivity. This synergy of methods can also create a more personalized approach to customer issues. Threats may become easier to manage. 

Understanding how these processes work alongside data sources and actual users can lead to better resource management and operational efficiency.  

If you need help setting up or improving systems for real-time data processing, Taazaa has a team of experts who can guide you in initiating data streaming architectures. As an established enterprise software product development company, we offer tools and various enterprise software pricing models that can help businesses thrive in the digital world. Contact us today to learn more.