Technology is constantly evolving. Big Data, for example, went from buzzword to business staple a long time ago, exploding and offering actionable insights on how businesses approach everything from HR to marketing analytics. Today, instead of just using data in retrospective reports or to project future trends, companies are using it to harness that insight in the now. It’s called real-time stream processing—and it just might change everything you thought you knew about Big Data.
What is Stream Processing?
Stream processing is a platform that allows businesses to implement rules and procedural approaches to examine real-time data alongside data at rest, ultimately detecting patterns at any given moment. Stream processing goes hand in hand with stream analytics—the ability to conduct a statistical analysis at any point within the data stream.
In short, stream processing allows a company to look at data in all phases—where it’s been, when it’s in motion, and where it’s going. Contrast that to a traditional setup in which data must be indexed and processed before it reaches its destination. Stream processing circumvents those steps, extracting the data while it’s being transferred and connecting it to external sources for timely applications.
Real-time stream processing systems must be robust because, according to Google Cloud, they’re dealing “with data import, processing, storage, and analysis of hundreds of millions of events per hour.” Yes, hundreds of millions per hour. Wondering how that architecture could possibly look? See Google Cloud’s version below.
The concept overall can admittedly be a little dense, so let’s look at some real-world examples to help bring it home.
Examples of Stream Processing
Electronic trading in the stock market is a classic example of stream processing in motion. As electronic trading is not a new phenomenon by any means, it’s clear even stream processing has undergone its own evolution. Today, stream processing is perfectly suited for the plethora of sensor applications related to the ever-connectedness of the Internet of Things (IoT). Factories that use sensor technology to calculate speed or output based on up-to-the moment data about capacity or energy usage, for example, cannot function without stream processing.
Think of virtually any IoT scenario, and odds are it is rooted in stream processing. Let’s examine some less obvious applications in the business world for some additional perspective:
- Brick-and-mortar businesses can track pieces of data from customers’ mobile devices—for example, their locations—and send targeted incentives when would-be shoppers are nearby.
- Financial institutions can see stock market fluctuations in real-time and rebalance portfolios based on accurately computed, up-to-the-minute risk assessments. Or, they could offer this same capability (at a fee, of course) for clients who want more control over their investments.
- Web companies can examine clickstream records across multiple websites, combine that with at-rest data like demographic information, and automatically determine what subsets of viewers best relate to particular campaigns or even content placement.
- Ecommerce companies or other financial companies can watch machine-driven algorithms and find patterns that might be suspicious, helping to detect fraud the moment it happens.
From those examples, we can glean some key benefits of stream processing:
- It can provide a pathway for more sophisticated data analysis.
- It can work in conjunction with machine learning to produce even deeper insights for companies of all kinds.
- It can help businesses boost efficiencies, thereby reducing costs and increasing output.
- It can assist brands in delivering more relevant consumer experiences—a key pathway to success in this age of digital-everything.
- It can provide a new host of capabilities when it comes to fraud detection and management, both in the financial industry and out of it.
Just a few short years ago, there weren’t many products on the market that could deliver real-time stream processing. That isn’t the case today—there are a host of options that offer not only stream processing but also management tools, a high degree of scalability, the ability to integrate with other key enterprise technologies, and more.
The future of stream processing is a bright one. Some take it even farther. In fact, Dr. Hossein Eslambolchi, Technical Advisor at Facebook, wrote on LinkedIn “The future of large size data streaming and innovation is more critical than any other innovation for the next decade.” I agree. With the explosion of the IoT and the exponential rise of connected mobile devices, stream processing is so much more than just the next big thing in Big Data. And, if stream processing isn’t already on your radar, it should be.
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This post was first published on Futurum.
Shelly Kramer is a Principal Analyst and Founding Partner at Futurum Research. A serial entrepreneur with a technology centric focus, she has worked alongside some of the world’s largest brands to embrace disruption and spur innovation, understand and address the realities of the connected customer, and help navigate the process of digital transformation. She brings 20 years' experience as a brand strategist to her work at Futurum, and has deep experience helping global companies with marketing challenges, GTM strategies, messaging development, and driving strategy and digital transformation for B2B brands across multiple verticals. Shelly's coverage areas include Collaboration/CX/SaaS, platforms, ESG, and Cybersecurity, as well as topics and trends related to the Future of Work, the transformation of the workplace and how people and technology are driving that transformation. A transplanted New Yorker, she has learned to love life in the Midwest, and has firsthand experience that some of the most innovative minds and most successful companies in the world also happen to live in “flyover country.”