In an era where data infrastructure is being radically reshaped by artificial intelligence, automation and data sovereignty requirements, Oracle Corp. is attempting to reset the playing field. With the launch of its globally distributed Exadata Database on Exascale infrastructure, Oracle is not simply extending its legacy capabilities into new markets, it’s making a bold claim to leadership in distributed data management for AI-native workloads.

The announcement comes amid growing competition in the distributed database space, where platforms such as CockroachDB, Yugabyte and Amazon Aurora are increasingly common choices for developers. But Oracle is leaning into its DNA, leveraging deep enterprise roots — full-featured SQL support and engineered systems — to assert a differentiated position. Oracle claims its new product is more than just another distributed database offering; rather the company says its latest move represents a convergence of infrastructure, database technology and AI readiness that few, if any, other vendors can match.

The underlying thesis is that as AI systems become embedded into mission-critical workflows, customers will need more than speed and scale; they’ll demand automation, consistency, high availability and compliance with data sovereignty laws. Oracle believes it can deliver all of the above in a package that promises a cloud-native, serverless experience that runs across geographies, clouds and business functions.

Distributed databases are having their moment

The distributed database market has evolved rapidly over the last decade, fueled by digital transformation and the globalization of applications. What began as a niche movement to overcome the limitations of monolithic databases has exploded into a core architectural strategy for modern enterprises.

But while many vendors rushed to build scale-out systems, Oracle came later to market but had the benefit of “going to school” on the market signals. Oracle has bet on its existing sharding capabilities and its heritage of high-scale, mission-critical deployments. What’s new with this announcement is Oracle’s decision to make these capabilities more accessible and cost-effective through Exascale, which is a serverless version of its engineered Exadata infrastructure.

As Wei Hu (pictured, right), senior vice president of high availability technologies at Oracle, explained in a recent interview with theCUBE’s Dave Vellante (left), “We consider [our offering] to be the most powerful, feature-rich distributed database with full SQL support.”

That phrase — “full SQL support” — became a recurring theme  in the conversation. (* Disclosure below.)

SQL compatibility: A non-negotiable for enterprise AI

Early in the big data era, NoSQL was all the rage; but the acronym evolved into “not only SQL,” and SQL became a necessary capability. Many early NoSQL players quickly realized this and were forced to bolt on the capability. Oracle claims that its distributed database was designed from the ground up to support full SQL syntax and data types. This is not an academic distinction. SQL remains the lingua franca of enterprise data. Attempts to force fit  SQL interfaces onto NoSQL engines, by often using translation layers or limited syntax subsets, have led to compatibility issues, performance complaints and often painful migrations. Oracle claims its approach avoids these pitfalls.

“What developers need is SQL,” Hu said, echoing the growing realization across the industry — even among early NoSQL innovators such Google — that SQL remains essential for building and maintaining enterprise applications. Oracle says it supports full data type coverage and SQL syntax out of the box, making it easier for organizations to lift and shift their applications into a distributed context without rewriting code.

This becomes critical in the AI era. AI agents, especially those operating autonomously at the edge or across multiple jurisdictions, need fast, consistent access to structured data. That’s not a use case that plays well with eventual consistency or partial SQL implementations.

Agentic AI and the new infrastructure stress test

One of the most notable aspects of the announcement is Oracle’s direct linkage between distributed databases and the emerging world of agentic AI. Unlike traditional software, agentic systems generate large, bursty, machine-driven traffic patterns and require immediate access to accurate, sovereign-compliant data.

“We’re seeing high-load, bursty traffic patterns that come in waves and must be handled elastically,” Hu said. “These agents must also meet data residency requirements — and all of this has to be always-on.”

Oracle is positioning its system as ideally suited for this class of workload. The combination of Raft-based active-active replication, multi-region availability and policy-based data distribution allows customers to manage high availability, scalability and compliance in one coherent platform.

Raft replication is used to ensure consistency across distributed nodes. In Oracle’s implementation, it allows for sub-three-second failover with zero data loss — even in the event of node or region failure. Oracle also supports two additional replication methods to account for variable network reliability, a feature aimed squarely at global deployments.

Compliance and data sovereignty at scale

One of the most pressing challenges for global enterprises is managing compliance with a growing patchwork of data localization laws. Countries such as India, China and EU member states are increasingly enforcing policies that restrict where data can reside and how it can be processed.

Oracle’s system addresses this through automated, policy-driven data placement. Customers can use predefined policies to ensure that data remains within specific geographies — even while applications operate across regions. As Hu explained, one major U.S. bank is using the system to store data separately in India and the U.S. while presenting a unified database view to applications running in the U.S.

This allows Oracle to support global operations without requiring enterprises to replicate full infrastructure stacks in every country — a cost-prohibitive proposition in smaller or emerging markets. Instead, Exascale allows customers to spin up lightweight deployments in specific jurisdictions, scaling resources elastically as needed.

This model promises to dramatically reduce the barriers to compliance and makes it economically viable for organizations to extend operations into new markets.

Automation, not just optionality

A key theme throughout the conversation was Oracle’s emphasis on automation. While the system offers extensive customization — supporting six data distribution strategies, including hash, range, list, composite and value-based methods — it also operates in an “autopilot” mode.

“We can do all of this for you,” Hu noted, pointing out that organizations don’t need to manage distribution manually unless they want to. “The application doesn’t know where the data is located. It’s completely transparent.”

This strikes at a critical pain point for many enterprises, particularly those with limited in-house database expertise. Developers and IT teams don’t want to be database administrators. They want to focus on building value. Oracle’s promise is to abstract away the complexity without compromising control.

AI, meet your data

Perhaps the most strategically important aspect of Oracle’s offering is its emphasis on co-locating AI with business data. In contrast to many AI architectures that involve lifting data into external stores for vector search and model training, Oracle is bringing AI to the data.

“Most companies store their business data in Oracle,” Hu said. “We want you to do AI with your data, right where it lives.”

By integrating vector search directly into the database engine and accelerating those searches with hardware optimizations via Exadata, Oracle enables real-time inference and retrieval-augmented generation (RAG) workflows directly within the data layer.

This convergence simplifies architecture, reduces ETL overhead and ensures data security and compliance. It also means that AI workloads benefit from the same enterprise-grade replication, availability and observability as transactional applications.

Economics of exascale

All of this would be less compelling if the economics didn’t pencil out. But Oracle says it is targeting price sensitivity with its Exascale model. Customers can start with as little as 4 ECPUs per shard and 300 GB of database storage per distributed location and dynamically scale both based on demand.

“Exascale allows you to start really small,” Hu explained. “If you’re establishing a presence in a new country, you can put a minimal footprint there and grow as the business scales.”

By separating compute and storage and offering serverless provisioning, Oracle is reducing the capital commitment required to stand up global data infrastructure — while preserving the full fidelity of its enterprise-grade database.

Strategic outlook: Is this a turning point?

While Oracle has long been a dominant force in enterprise databases, it has struggled at times to shake perceptions that it is late to cloud or too wedded to legacy models. The success and momentum of Oracle Cloud Infrastructure (OCI) has gone a long way in addressing these criticisms. This release may mark another proof point. Oracle is not just adapting to the new era, it’s applying a new architectural paradigm to its proven business model.

By combining full SQL support, data sovereignty compliance, active-active replication and embedded AI capabilities in a serverless, elastic form factor, Oracle is presenting a compelling vision of what distributed data infrastructure can and should be in the AI-native enterprise.

As organizations shift from AI experimentation to production deployments that require high availability, explainability and compliance, Oracle is betting that the market will favor platforms that offer rigor over novelty.

When asked for customer proof points, Oracle provided the following to theCUBE:

“Providing exceptional customer satisfaction is important to PayPal, so we’ve been using Oracle Exadata for many years to provide lightning-fast response times and mission-critical availability,” said Akash Guha, director of database engineering at PayPal. “As our global business grows, we plan to provide even faster responses by using distributed solutions that are integrated with our core systems of record to provide extreme availability and performance. We look forward to using Oracle Globally Distributed Exadata Database on Exascale Infrastructure’s always-on, serverless architecture with built-in Raft replication to accelerate responses, enable greater application resilience and lower costs with scalable resources.”

Final word

Oracle’s globally distributed Exadata Database on Exascale infrastructure isn’t just an incremental upgrade; it’s a strategic refactoring of how enterprises should think about AI and data at global scale. The product signals Oracle’s ambition to be more than a legacy stalwart in the cloud era; it positions the company as a serious, forward-looking contender in the battle to define the future of intelligent infrastructure.

For enterprise architects tasked with building globally compliant, always-on, AI-infused data systems, Oracle’s new platform deserves a place on the shortlist. The combination of maturity, modernity and mission-critical readiness makes it a serious alternative to both cloud-native upstarts and hyperscale incumbents.

With more details expected at Oracle CloudWorld in October, the industry will be watching closely to see if Oracle can execute on its vision and whether the market is ready to meet it there.

Here’s theCUBE’s full video interview with Oracle’s Wei Hu:

(* Disclosure: Oracle Corp. sponsored this segment of theCUBE. Neither Oracle nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE