The Speed of Now: Examples of Real-Time Processing in Action

The Speed of Now: Examples of Real-Time Processing in Action

Wojciech Marusarz - April 17, 2023

Data is the lifeblood of modern businesses and organizations. The ability to process and analyze data in real-time can be the difference between success and failure. Real-time processing allows organizations to make quick, informed decisions based on up-to-the-minute data, resulting in improved accuracy and increased efficiency.

From financial services to healthcare to logistics and supply chain management, real-time data processing is being used across industries to gain a competitive advantage. In this article, we’ll explore some real-world examples of how real-time processing is used to process and analyze large amounts of data.


• Real-time processing enables organizations to analyze data in real-time and make quick, informed decisions based on up-to-the-minute data. Real-time stream processing is different from batch processing, which involves processing data in batches rather than to continuously process data streams as they are generated.

• Real-time processing is being used across industries, including financial services, healthcare, retail, telecommunications, logistics and supply chain, manufacturing, advertising, and energy and utilities.

• Real-time processing systems can be used for monitoring stock prices, tracking transactions, detecting fraud, remote patient monitoring, customer behavior analysis, inventory management, fleet and cargo tracking, process control, predictive maintenance, and more.

• Real-time data processing provides several benefits, including faster decision-making, improved accuracy, increased efficiency, and enhanced customer experiences.

• Ready to implement real-time processing in your business? Contact nexocode’s team of expert data engineers today to see how we can help you unlock the full potential of stream processing systems.

The Need for Speed in the Modern World and the Movement Toward Data Streams

The ever-increasing demand for swiftness in many aspects of life nowadays is undeniable, as is the growing volume of data being created by and made available to us. Organizations of all sizes must now be able to capture, process, and then analyze large amounts of this data in real-time to remain competitive in their respective industries.

As such, there has been a shift from the traditional batch processing of data to a more agile, data stream approach. This enables processing large volumes of incoming data and analyzing it in real-time, therefore facilitating faster decision-making with greater accuracy thanks to it being based on more up-to-date insights.

Batch Processing vs. Stream Processing

The batch processing method is a distinct concept from real-time processing – the former completes a number of jobs collected together to form input data groups, whereas the latter will continuously process data streams as they are generated.

Batch Data Processing

Real-time processors are capable of handling large numbers of repetitive jobs on a regular basis, increasing the efficiency of some particular data processing tasks such as making backups, sorting data, and applying filters. When performed individually, these tend to be compute-intensive, but processing time can be reduced when grouped into batches.

How traditional batch architectures work? Data gathered over a period of time can be later on processed in data sets (batches) to produce analytics

How traditional batch architectures work? Data gathered over a period of time can be later on processed in data sets (batches) to produce analytics

A company may only run its payroll system once a month, in which case it would be much more efficient to group these tasks into batches and run them together than make one-off payments. However, this approach can cause delays in decision-making and reduce accuracy, as the data providing information may be out of date by the time action is taken.

Stream Data Processing

In contrast, stream processing relies on real-time processing of data streams as they are generated, enabling organizations to make decisions based on up-to-the-minute information. 

How continuous data stream processing work? Up to milliseconds of time delay data streams can be utilized for real time analytics and other use cases

How continuous data stream processing work? Up to milliseconds of time delay data streams can be utilized for real time analytics and other use cases

Real time processing architecture requires a way to capture incoming messages, process them as they arrive, and stream forward to another service (stream-processing consumer) or write them, to an analytical data store (where it can be used for batch data processing later on). A solution like this tends to need a message broker, which is there to support scale-out processing and reliable delivery in order to buffer the received messages. The stream processing engine runs queries or machine learning models right on real time data to instantly produce insightful results. Read about stream processing use cases here.

Continuous stream processing - stream processing tools run operations on streaming data to enable real time analytics

Continuous stream processing - stream processing tools run operations on streaming data to enable real time analytics

Since real-time data processing facilitates faster decision-making, this stream-based approach provides valuable insights that can be used in such a way as to provide better customer experiences, improve accuracy, increase efficiency, and even generate new revenue streams.

Examples of Real-Time Processing Systems

There is a wide range of industries where real-time processing has been implemented to speed up decision-making and improve efficiency. Below are several good examples of implementations of how organizations across multiple industries are utilizing stream processors to gather and prepare data for analysis.

Streaming data processing architecture for continously generated streams of events to be processed and provide data for further applications

Streaming data processing architecture for continously generated streams of events to be processed and provide data for further applications

Real-Time Data in Financial Services

In the financial sector, real-time processing is used to track stock prices, process and monitor payments, and detect fraud, among others. This facilitates the flagging up of any sudden or unexpected fluctuations in stock prices, suspicious transactions, or malicious actions as quickly as possible so that immediate action can be taken if required.

For example, in a financial services context, stream processors allow organizations to detect and prevent fraud as soon as it occurs rather than retrospectively. It also allows businesses to analyze customer behaviors in real-time, providing valuable insights such as customer spending patterns, preferences, and likely upsell opportunities.


The healthcare industry also benefits from stream data processing thanks to the possibility of faster diagnoses and treatments. Real-time patient data analysis means that doctors and nurses, etc., always have access to the most recent information on electronic health record (EHR) updates, even remotely, and are able to flag any potential issues when they arise.

The most important aspect of real time data in healthcare is for remote patient monitoring: Wearable devices, such as fitness trackers and smartwatches, continuously generate data about patients’ vital signs and other health parameters. Stream processing can analyze these data streams in real-time, allowing healthcare providers to detect anomalies, make timely interventions, and improve patient outcomes.

Furthermore, healthcare professionals can more accurately assess each patient’s condition and create personalized treatment plans for them thanks to the data-driven insights gleaned from stream processing systems. The potential results of this are significant improvements in patient outcomes and reduced waiting times.

Retail and eCommerce

Data streaming and real time data analysis helps businesses in the retail industry, whether online or brick-and-mortar stores, improve the customer experience by collecting data from existing and potential clients. Analyzing data on, e.g., customer behavior in real-time lets eCommerce retailers offer personalized recommendations, discounts, and promotions.

Not only this, but retailers can also track and manage their inventory more effectively and respond to supply chain issues in real-time to offer faster delivery times and implement dynamic pricing strategies. As in the financial sector, fraud detection is also possible in eCommerce thanks to stream processors immediately flagging up any suspicious activity.


Telecom companies are able to monitor network traffic in near real-time by utilizing stream processors and identify any issues that may be causing congestion. As a result, businesses in this sector can optimize their networks and maintain reliable services for their customers without suffering any significant downtime.

What’s more, analysis of call record details may be conducted to identify any potential customer service issues and address them before they become bigger problems. Real-time processing is also put to use in telecom companies to detect malicious activities such as hacking attempts, phishing, and more.

Logistics and Supply Chain

Stream processing is an invaluable tool for real-time logistics management that allows businesses in this industry to monitor their fleet of vehicles and cargo, track and optimize shipments and deliveries, support dynamic pricing solutions, and detect any irregularities or delays. The potential upsides include faster, more efficient delivery times and improved customer service systems.

Fleet sensor data processing, for instance, is used to provide accurate and up-to-date information on the location of vehicles and shipments, their estimated times of arrival, and how they are performing in terms of fuel consumption or predictive maintenance. Traffic control systems, meanwhile, can identify congestion and help goods reach their destinations according to schedule.


Manufacturers are able to take advantage of real-time processing solutions for monitoring and controlling their production lines and processes to keep them operating at optimal levels. One application of this is industrial quality control to detect any defects in a product in a cost-effective and efficient way by stream-processing various production data from sensors and images of it.

Moreover, data generated from sensors can be analyzed in real-time to detect anomalies in the manufacturing process, such as faulty components or unexpected delays, so that predictive maintenance can be carried out if necessary. The results of this can be better quality products and improved customer satisfaction.


Stream processing is being used by advertisers for campaign management, tailoring their message to consumers according to data collected about them and analysis of their user behavior. This contributes to the creation of far more effective campaigns, targeting the right people with the right message at the right time.

Real-time bidding is also becoming increasingly popular because it enables marketers to place ads live within web pages or apps, often thanks to the power of artificial intelligence, and only pay for actual impressions. This brings greater cost efficiency, increases the likelihood of conversions, and improves the overall customer experience.

Energy and Utilities

Stream processing allows energy and utility businesses to monitor power grid usage, detect any disruptions in the flow of power or water, then automatically adjust supply levels with the aim of avoiding outages. Stream processors can also forecast energy demand through real-time analysis of data gathered from sensors on smart meters.

The advantages that these solutions bring include better supply management, improved customer service, and substantial cost savings. Finally, renewable energy management systems can be implemented by using stream processing to optimize the use of renewable sources like wind and solar power in real-time.

Real-Time Processing: The Key to Unlocking Digital Transformation

Businesses of all types and industries are realizing the potential of real-time processing for driving operational efficiency and delivering improved customer experiences. Stream processors not only let them react quickly to changes in their environment but also anticipate and predict the needs of their customers.

It is clear from the real-time processing examples covered here that this technology is becoming increasingly important to stay ahead of the curve, and its possibilities are virtually limitless. Therefore, organizations that decide to leverage the power of stream data processors businesses can take their operations to the next level.

If, after reading this article, you feel ready to implement real-time processing in your business, expert data engineers at nexocode are on hand to assist you with unlocking the full potential of stream processing systems. Contact us today to start your digital transformation or find the answers to any questions you still may have.

About the author

Wojciech Marusarz

Wojciech Marusarz

Software Engineer

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Wojciech enjoys working with small teams where the quality of the code and the project's direction are essential. In the long run, this allows him to have a broad understanding of the subject, develop personally and look for challenges. He deals with programming in Java and Kotlin. Additionally, Wojciech is interested in Big Data tools, making him a perfect candidate for various Data-Intensive Application implementations.

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