The sheer volume of data and the pace at which it is generated by companies globally is driving the need for real-time data processing and analysis. It’s simple, when companies want to be as efficient as possible, they need real-time insights into their data that allow them to improve their business processes, make better decisions, serve their customers better, and generate more revenue.
Considering the above, it’s easy to see why stream processing is becoming increasingly popular with many companies. Simply put, it helps them gain more value from their data and sets them up for continued success.
So, now the question is obviously whether you should use stream processing in your business? In this post, we’ll look at this question in more detail and show you whether stream processing is right for your business.
Traditionally, before the advent and popularity of stream processing, if you wanted to gain insights from your data, you’d typically use batch processing to analyze it at specified times. However, times have moved on, and with the speed at which data is generated, not all insights are created equal. For example, a specific data point might have value once it’s created but that value could diminish quickly as time passes. As a result, to get the value from the data it should be analyzed before its value diminishes.
And that’s where stream processing comes in. Unlike batch processing, where data is analyzed at a specific time, stream processing focuses on the real-time processing and analysis of data in motion, or, in other words, when it’s gathered.
This enables you to query and analyze continuous data streams, get faster insights from their data, and react quicker. Because of this, you’re able to spot trends earlier, capitalize on opportunities faster and make your business processes much more efficient and robust. amounts of data are processed at once.
Typically, stream processing is useful in situations where you want to detect a problem or issue and improve the outcome by solving it faster. It’s also quite common where you need to rely on real-time data for the efficient functioning of a platform or system.
So, you would generally find stream processing in applications like algorithmic trading and stock market surveillance, traffic monitoring, supply chain optimization, geospatial data processing, and sports analytics.
In addition to these, stream processing also plays a vital role in any data-driven organization. With that in mind, let’s look at some of the common use cases for stream processing.
Log analysis is one of the most common use cases for stream processing in IT operations. Here, it can query continuous computer system and network monitoring data streams and process the received data.
As a result, it’s able to identify anomalies and incidents before it reaches the end-user. For example, if you have an application, the last thing you want is downtime because it negatively affects the user experience and your reputation with your customers.
With the valuable data you get from log analysis, you will be able to detect any issues earlier and fix them before the application goes down.
With stream processing, you’re able to detect anomalies in transaction data in real-time which, in turn, signals possible fraud. So, when you use this method of surveillance and fraud detection you’ll be able to identify fraud and stop transactions before they’re completed.
In addition, machine learning algorithms can analyze vast amounts of transaction data in real-time to identify patterns and trends in the data. With these patterns analyzed, these algorithms can then identify possible fraudulent transactions.
Cybercrime is becoming an increasingly worrying problem for companies around the globe. It’s therefore vital that you have the necessary cybersecurity systems in place to prevent attacks and data loss.
Here, stream processing can be extremely helpful. It allows you to process and analyze streaming data end detect anomalies which helps you identify security issues in real-time. In turn, you’re then able to isolate any security threats in your systems. Ultimately, this makes your security measures more robust.
Another common use case for stream processing is sensors and devices. Here, you might think of it as being useful in smart device applications or with smart patient care platforms. But its uses go far further than this. It’s, for instance, also used for production line monitoring and applications like geofencing and wildlife tracking.
It can also be useful in aircraft where identifying issues and carrying out predictive maintenance can be crucial. Likewise, it’s commonly used in the oil and gas industry to monitor various processes during petroleum production and refining, which in turn ensures integrity in the process.
From a business perspective, stream processing can also be extremely valuable for a company’s digital marketing efforts. It can, for example, track user behavior, user clicks, and user interests based on web analytics data good licked it from the user.
With this data, you can then ensure that your products or services or placed in front of the right people at the right time. if you’re able to do this, you increase the likelihood of a sale and you’re able to generate more revenue.
In recent years many companies have migrated away from on-premises IT infrastructure and have moved to the cloud. When they do this, they need to migrate their data from their on-site storage to the cloud which can be a long and challenging process.
Stream processing can simplify this process by moving the data from the on-site storage to the cloud in a stream of data instead of doing it in batches. This minimizes the complexity and reduces the possibility of errors.
So, now to answer the question, should you use stream processing in your business? The answer is simple, stream processing is ideal if you need real-time insights into your data that allow you to:
When you decide that you want to implement stream processing in your business processes, why not have a look at Scramjet? It uses the efficiency and scalability of stream processing, while being cost-effective and developer-friendly which makes it easy to reap the benefits from your data.
To learn more about our platform, visit our website scramjet.org for more details.
Photo by Markus Winkler