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Theres no talking about the Summer of Data without including a significant addition about the year of artificial intelligence the two are inextricably linked and will remain so in the months to come.

Thats because the rise of AI has led to the need for incredible amounts of data, and projections indicate that data centers are to become the worlds largest energy consumers, rising from 3% of total electricity use in 2017 to 4.5% by 2025. Indeed, more companies are seeing their data needs grow on a yearly basis, leading to the characterization that every company is a data company.

With an eye on approaching this challenge, various technological advancements have rolled out in 2023, including a necessity for data storage innovation. Next-generation storage solutions are estimated to be valued at more than $150 billion by 2032, according to a recent studyfrom Global Market Insights Inc.

Its also no surprise that every vendor offering data-related solutions are striving to secure a share of whats estimated to be a total addressable market in the tens of billions of dollars when it comes to data platforms, noted Rob Strechay, lead analyst for theCollective from theCUBE, in an analysis for SiliconANGLE.

The opportunities for storage platform vendors and data platform vendors lie in integrating data platforms as-a-service into their storage offerings, Strechay wrote.

With all of these changes and demands in mind, some of the major players in data including Snowflake Inc., MongoDB Inc., VAST Data Inc. and Databricks Inc. spent the Summer of Data unveiling their strategies as data becomes even more important in support of AIs evolution.

Though all of these companies and others like them are responding to the same challenges, their solutions differ. Thats why, with the Summer of Data in the rearview, its worth a recap of what we learned so far and where these companies could be heading next.

This feature is part of SiliconANGLE Medias ongoing series with theCUBE exploring the latest developments in the data storage and AI market.

Before this years Snowflake Summit, the companys stated target of $10 billion in revenue for fiscal year 2028 left plenty of open questions about how they might get there. Over the course of this year, meanwhile, theCUBE has produced a number of in-depth analyses, laying out a mental model for the future of data platforms.

In his post-summit analysis, theCUBE analyst Dave Vellante discussed the vision outlined by Snowflake during this years Snowflake Summit, from its keynote presentations to product announcements. The companys intention was clearly to be the number one platform on which this new breed of data applications will be built, according to Vellante.

This weeks Snowflake Summit further confirmed our expectations with a strong top-line message of All Data/All Workloads, and a technical foundation that supports an expanded number of ways to access data, Vellante wrote. Squinting through the messaging and firehose of product announcements, we believe Snowflakes core differentiation is its emerging ability to be a complete platform for data applications. Just about all competitors either analyze data or manage data.

Other companies have also been weighing their strategies as the world of data storage evolves and as data and AI converge. For VAST, that looks like an evolution beyond being a storage company. In early August, VAST announced a new, global data infrastructure for AI called the VAST Data Platform, with an aim to unify data storage, database and virtualized compute engine services in a scalable system.

By bringing together structured and unstructured data in a high-performance, globally distributed namespace with real-time analysis, VAST is not only tackling fundamental DBMS challenges of data access and latency but also offering genuinely disruptive data infrastructure that provides the foundation organizations need to solve the problems they havent yet attempted, Market Strategy analyst Merv Adrian said at the time of the announcement.

Meanwhile, the realities of modern business with challenges such as the skills shortage considered mean developers must be kept happy. That has been good news for companies such ascloud database provider MongoDB.

The company recently saw its stock soar with blowout fiscal first-quarter earnings results, which posed an interesting question to watch in advance of MongoDB .local NYC in June: Was AI contributing to the surge in stock price?

DevOps democratization has surged over the past 20 years, but AI has posed a new wrinkle. AI isnt the only thing to consider as developers seek to go next-level with their data, according to Mark Porter, chief technology officer of MongoDB, during an interview with theCUBE during MongoDB .local NYC.

It is currently the thing thats really exciting, and being able to build great apps that do great things with your core data is always going to be important, he said. But whats happening is people are enhancing their apps with AI.

With hundreds of people using MongoDB as the foundation of their AI apps, Porter pointed to the companys developer data platform as key to this arrangement. Meanwhile, Databricks recently acquired Okera Inc., a data governance platform with a focus on AI with a stated goal to expand its own governance and compliance capabilities when it comes to machine learning and large language model AIs. Customers used to control access to their data using simple data controls that only needed to address one plane, such as a database.

The rise of AI, in particular machine learning models and LLMs, is making this approach insufficient, the Databricks team, including Chief Executive Officer Ali Ghodsi, explained in the announcement.

As an industry leader, many are watching what Databricks is doing closely, including Vellante. The big question for the company this summer was surrounding how it would execute its critical strategic decisions in the future as hype and confusion continued to swirl around the world of AI.

Emerging customer data requirements and market forces are conspiring in a way that we believe will cause modern data platform players generally and Databricks specifically to make some key directional decisions and perhaps even reinvent themselves, Vellante wrote in an edition of his Breaking Analysis series.

After the Data + AI Summit, those connections began to come into better view. In a new world where data is influenced by broader trends in AI, Databricks is back in its wheelhouse, according to Doug Henschen, vice president and principal analyst at Constellation Research Inc.

I think generative AI for the last three years, theyve been building up the warehouse side of their Lakehouse and making a case, he said. All this time data science has been their wheelhouse, and their strength and their customers are here, while others are making announcements of previews thatll help eventually down the road on AI. This is where its really happening, and theyre building generative models today.

TheSummer of Data may be over, but its clear that the evolution of AI will continue to play into the strategy of major players in data for many months to come. That will lead to solutions such as the adoption of next-generation storage solutions and their valuation at over $150 billion by 2023.

Though the AI-powered hybrid-multi-super cloud comes with various demands on data, companies such as those mentioned above have laid out their plans during the Summer of Data, and the year ahead will be critical as those same companies are tasked to execute. So, too, will various data platforms continue to evolve.

Most traditional applications are built on compute, networking and storage infrastructure, but the future will see applications program the real world, George Gilbert, a contributor to theCUBE, wrote in a recent analysis.

In that world, data-driven digital twins representing real-world people, places, things and activities will be on the platform, Gilbert wrote, which explains the stakes at hand.

On balance, we believe that the distributed nature of data originating from real-world things, combined with the desire for real-time actions, will further stress existing data platforms. We expect a variety of approaches will emerge to address future data challenges, he wrote. These will come at the problem starting from a traditional data management perspective (such as Snowflake), a data science view of the world (such as Databricks) and core infrastructure prowess (cloud/infrastructure-as-a-service, compute and storage vendors).

Clearly, the challenges around data remain the same as AI continues its meteoric rise. The upcoming months will be critical as the needs of companies in this new world continue to be of paramount importance.

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With the summer of data in the rear-view mirror, here are the key ... - SiliconANGLE News