Real-time data will shape the next wave of innovation

Rapid advances in technology aren't just a buzzword; they're driving today's most significant innovations. Once a niche aspect of technology, real-time data has now taken center stage, influencing everything from artificial intelligence to the cloud ecosystem.

“We've been seeing for a few years now that if you're going to build an ecosystem on the cloud, you're going to need a platform, and the platform is going to need data,” said John Furrier during a recent discussion at SiliconANGLE Media's livestreaming studio, theCUBE. “We've always said that the role of data is so critical that developers are not just going to use data, they're going to code around data.”

This shift is made even more pronounced by generative AI, making the role of data not just important but crucial: According to a report from Synergy Research Group, the number of large data centers operated by hyperscale providers will exceed 1,000 in early 2024. This explosive growth highlights a new paradigm in which access to and management of real-time data is not just an advantage, but a necessity to remain competitive in a rapidly evolving technology environment.

Meanwhile, with the number of facilities growing rapidly and average capacity continuing to increase, it only took four years for the total capacity of hyperscale data centers to double, the research report states. Looking forward, Synergy predicts that the total capacity of hyperscale data centers will double again over the next four years.

This feature is part of SiliconANGLE Media’s research into real-time data trends ahead of Aerospike Inc.’s Real-Time Data Summit, taking place June 25-26.*

How real-time data and AI are transforming development and computing power

Data being generative means generating the execution environment in which data and content can be assembled. According to Furrier, this represents a kind of shift left for developers.

“We're starting to see early signs with the multimodal capabilities of large language models and complex neural networks,” he said. “We're really heading in that direction. From a developer perspective, all the hype and all the action is there. This is the perfect storm that's going to shift the game a bit from where it was before and move it into a new paradigm.”

Harnessing this new wave of processing power will no doubt be a dynamic and competitive challenge. Consider the ongoing global battle to control the future of computing power.

The current battle involves some of the world's largest technology companies, including Google, Apple, Microsoft, Amazon and Meta, making their own AI chips because they want to control a critical asset and therefore the destiny of their own companies, Casey Logan, senior principal of supply chain at Gartner, said in a recent report.

The future path of innovation in the new generation world of AI is by no means clear. However, access to real-time data and the ability to process vast amounts of data from a variety of sources is becoming the norm. Companies such as Aerospike Inc. have been working with real-time data for years. However, it is becoming increasingly clear that customers will need to leverage multiple streaming technologies simultaneously to manage this vast amount of data, including solutions such as Apache Kafka.

For those just graduating from college or those already established in their careers, what does the journey ahead look like? And when the pace of innovation is so fast, how does everyone keep up with it?

A Paradigm Shift in Higher Education

Even though generative AI was still in its infancy, the question quickly arose: “Should we rethink college majors in this new age of AI?” After all, generative AI is changing everything, and that seems to be true in higher education as well.

Recently, The Wall Street Journal published an article about how computer science is more popular than ever at U.S. universities. But that doesn't necessarily translate to immediate careers. Still, the high demand for AI talent at companies like Amazon.com Inc., Nvidia Corp. and Meta Platforms Inc. is leading to big changes. According to Tim Foulkes, chief developer advocate at Aerospike, this represents a paradigm shift.

“The technology is evolving so quickly, and the ecosystem is evolving so quickly, that someone coming out of college is more knowledgeable than someone who's going to be mentoring them,” Foulkes said in a recent interview with theCUBE. “There are so many moving parts, it's so novel. How do they all fit together? For an experienced developer, it's almost frustrating to hear all of these terms. How do you put it all together?

Of course, various domain-specific products can be designed to meet the specific needs of individual industries, such as e-commerce or healthcare. But the ongoing paradigm shift brings new challenges.

Computers weren't made to hallucinate. This is a side effect of the move to new platforms. And the move to new platforms has changed us from the old way of doing things, where you just coded something and got a response,” Farrier said in a recent interview. “Things are programmed and on some level deterministic. Now we have new models where we don't know what we're going to get, and in many cases it's not the same answer because it's generated and the data is driving it.

Using AI to Overcome New Data Processing Challenges

Whereas once real-time data was focused on specific applications, it is now going mainstream thanks to AI's ability to quickly process and analyze vast amounts of information. This shift is revolutionizing industries and improving decision-making and operational efficiency across the board.

Apple noted at the recent Worldwide Developer Conference that it's handling inference on-device. There's an end-to-end work stream from the device to the core. Horizontal scalability is key, but you need low latency, Furrier said. It's a technical challenge.

Given these challenges, what do we do? For Aerospike, it's clear that we need to act now to solve the real-time data challenge. According to Lenley Hensarling, Chief Product Officer at Aerospike Inc., the mashup of external and internal data is going to be crucial. Being able to provide real-time, up-to-date data is going to be key to differentiation and competitive advantage.

The most important thing companies can do is not just ingest that data, but make it available to other applications, whether that's ML for training, creating context for making decisions, or data streams that show signatures or signals that need to make a decision in that moment, Hensarling said.

The key is to have all that data ingested so it's readily available, Hensarling added. A variety of tools make this possible, including Databricks, Confluent and Redpanda.

LLM and Understanding Real-Time Data

After all, how do we all keep up with innovation, and how do we best serve developers who are asking for a readiness plan? For some, this means turning to learning tools and readiness frameworks.

But developers must also prepare to build robust applications with multimodal capabilities. The first part involves large-scale language models, Foulkes said.

“You have to be able to understand what you're doing with the input data,” he said. “Some of your input data could be video or audio or even business objects. So if you're building a recommendation engine, you're going to need business objects that represent your data. It's a combination of all these elements. And then to do something useful with it, you need to use large language models.”

But without the context of an LLM, you might hallucinate. It's also important to keep in mind that an LLM isn't about writing code, it's about writing English.

“If you have a law degree, some rapid engineering, and a vector database, you can take all the inputs and pull the right information to give you an answer,” Foulkes said. And accuracy. This is a fuzzy search. You're not really looking for the exact answer. You're looking for something related to it.

With a traditional relational database, if you give the same input to an LLM, it will always return the same answer. But with fuzzy search, things are different.

There will be a set of ecosystem tools. Is this the right answer? How do you actually know that it's an approximate nearest neighbor search, which is often used in vector databases? How close is it? Is it right or wrong? Faulkes said.

While developers are sure to face more challenges in the coming weeks and months, it's clear that the convergence of cloud computing, generative AI, and real-time data requires a cohesive approach. While each element may still be in flux, adopting a unified strategy seems essential in this new era.

(* Disclosure: theCUBE is a paid media partner for advance coverage of the Real-time Data Summit event. Neither theCUBE coverage sponsor Aerospike Inc. nor any other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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