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2023 was a huge year for real-time data processing. Enterprises are more focused than ever using real-time data to guide the path forward, and things like AI are changing the game. As technology continues to evolve, real-time data processing has transformed from something closer…
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Over the last year or so, artificial intelligence (AI) has evolved from something most people consider science fiction to an increasingly accessible technology anyone can use. Thanks to the rise of tools like ChatGPT and DALL-E, AI has rapidly emerged as a transformative…
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In the era of big data, organizations are grappling with vast amounts of information that can hold immense value if harnessed effectively. Traditional methods of real-time data processing and analysis are no longer sufficient to handle the complexity and scale of modern datasets. That’s…
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As 2023 gets going, most companies are still pretty obsessed with “data”—as we are. Data is “the new oil”. You knew that already. But trends within the vast data realm come and go and some of them are clearly coming while others are clearly…
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Why Enterprises Need Contextual Decisioning The world today is faster-paced than ever before, and the explosive growth of both 5G and the Internet of Things (IoT) is generating a greater variety of data at extremely high velocity. As a result, enterprises are…
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There’s a lot of buzz around how 5G will impact industry 4.0, but there’s not a whole lot out there about why it’s important to consider upgrading to 5G to benefit your company’s bottom line. Seeing this gap, we worked with experts…
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Recently, experts from Volt Active Data presented a webinar, 3 Keys to Unlock 5G Monetization: Real-time Data, Automated Decisions, BSS Modernization. The webinar focused on the ways that moving to 5G monetizes for the company making that technology investment. Attendees asked a…
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Traditional pub/sub systems such as Apache Kafka (and their numerous ever evolving stream processing avatars like Apache Samza, Apache Storm, Apache Flink, etc.) work fine for a bunch of simple use cases where there is a need for simple decisions, speed, scale…
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Traditionally advanced analytics was utilized primarily to gain an historical understanding of business performance. Data scientists spent countless hours mining, munging, and model building; wasting expensive resources and valuable time only to get a rear view understanding of business performance. Starting Now:…
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Congratulations! You’ve mastered machine learning and can now generate a model that will help your enterprise succeed. There’s just one problem. Or rather several, actually. Below we discuss the challenges you face getting an ML model into production and how Volt Active…
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There is a massive difference between generating the input data needed by a Machine Learning model once (to prove a concept), and doing it continuously, indefinitely, at scale, and within short time periods. Years of experience working with disparate and imperfect data…
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In my previous blog on big data analytics, we discussed how to apply your big data analytics to real-time applications. The idea is that, if you have built analytics on your data, the next step is to use the analytics directly in the…