What is stream processing? How does it help with data analytics?

Stream processing enables users to send data through to analytics - minus the storage, learn how here.

How does PayPal process billions of transactions? The second quarter of 2019 revealed that the online platform processed $172 billion worth of payments. Millions of people around the world use PayPal to buy goods online, but how much does anyone actually know about stream processing, the technology that powers the online platform?

That’s why in this article, I’ll be discussing the technology that satisfies millions of customers every single day.

What is stream processing?

Let us start with the basics – what is stream processing? This technology allows systems to process data continuously and detect conditions within seconds. In other words, stream processing receives and analyses data in a continuous stream without delays. In the past, data was stored in a database and prepped for analysis. Stream processing allows users to skip storage and go straight into analysis allowing users to gain insights at a faster rate than before.

Stateful stream processing

There is a subset of stream processing called stateful stream processing. Stateful stream processing works because the computer maintains a contextual state, a state that stores data on previous events. You will find this streaming method used in several applications, like fraud detection, where the computer keeps the list of past credit card transactions and compares it against current transactions to detect fraud.

Stateful stream processing merges real-time applications and value store tables (database) into a single entity. The merging of two different entities leads to several benefits, including operational simplicity, data consistency, high performance and scalability.

Does stream processing augment analytics?

Stream processing allows data analytics to generate real-time insights, which is just one of the major advantages of real-time processing.

Ideal for large data volumes

Conventional processing methods are not suited for volumes of data. Conventional processing methods, like batch processing, are slower because they cannot stream and process data in real-time. Instead, data is collected first, then analysed. This method is not suitable for big data because insights will be generated at an incredibly slow rate. However, stream processing allows data to be processed in real-time, which significantly improves processing speed, especially for big data.

Less hardware

Stream processing allows data to be processed in real-time. Hence, there is no need for hardware to collect and store data. This reduced dependence on hardware reduces the cost of collecting and processing data, while also improving the speed of analysis.

Value in real insights

Not all insights deliver equal value, while some have a longer-term value that can transform a company’s fortunes – others have value in the short run and that value declines as time passes by. This is where stream processing is a huge asset. The real-time capability of stream processing generates the insight that holds tremendous short-term value for users.

Data streams from IoT

IoT devices like smart TVs, wearables, smart meters and commercial security systems will stream a lot of data – information that will provide plenty of valuable insight into their functions and usage. As a result of this, they will play a huge role in the consumer and business markets. For example, the value of industrial IoT will amount to $922 billion by 2025. Hence, data analytics with stream processing will be a great asset for IoT devices compared to other forms of processing. Organisations would need to prepare for the future by investing in analytics with stream processing to generate maximum value from their industrial IoT devices.

Streamlining the architecture

Stream processing has streamlined the architecture because it unifies applications and analytics. The unification allows developers to simplify the overall architecture of a system and develop applications at a faster rate. Moreover, developers can create applications that can respond to insights in quick time, which means less time between discovering insights and taking action. Therefore, developers can work at a faster rate to develop modern applications that can operate in an event-driven environment, thanks to stream processing.

Data analytics in real-time

Data analytics processes big data to deliver insights but technology with stream processing allows analytics to generate real-time insights. Stream processing is a vital asset because it expands the capability of analytics and gives developers more options to streamline technology and makes the most of the current technologies set to exert a huge impact on industries.

After all, we are already seeing the impact of real-time processing with the likes of PayPal, Netflix and a host of other technologies making full use of the technology.


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