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 shouldn’t be simply extending its legacy capabilities into recent markets, it’s making a daring claim to leadership in distributed data management for AI-native workloads.
The announcement comes amid growing competition within the distributed database space, where platforms equivalent to CockroachDB, Yugabyte and Amazon Aurora are increasingly common decisions for developers. But Oracle is leaning into its DNA, leveraging deep enterprise roots — full-featured SQL support and engineered systems — to say a differentiated position. Oracle claims its recent product is greater than just one other distributed database offering; relatively the corporate 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 turn into embedded into mission-critical workflows, customers will need greater than speed and scale; they’ll demand automation, consistency, high availability and compliance with data sovereignty laws. Oracle believes it could deliver all the above in a package that guarantees 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 past decade, fueled by digital transformation and the globalization of applications. What began as a distinct segment movement to beat the constraints of monolithic databases has exploded right into a core architectural strategy for contemporary enterprises.
But while many vendors rushed to construct scale-out systems, Oracle got here later to market but had the advantage of “going to high school” in the marketplace signals. Oracle has bet on its existing sharding capabilities and its heritage of high-scale, mission-critical deployments. What’s recent 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 chairman of high availability technologies at Oracle, explained in a recent interview with theCUBE’s Dave Vellante (left), “We consider [our offering] to be probably the most powerful, feature-rich distributed database with full SQL support.”
That phrase — “full SQL support” — became a recurring theme within the conversation.
SQL compatibility: A non-negotiable for enterprise AI
Early in the large data era, NoSQL was all the fad; however the acronym evolved into “not only SQL,” and SQL became a needed capability. Many early NoSQL players quickly realized this and were forced to bolt on the aptitude. Oracle claims that its distributed database was designed from the bottom as much as support full SQL syntax and data types. This shouldn’t be a tutorial distinction. SQL stays 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 infrequently painful migrations. Oracle claims its approach avoids these pitfalls.
“What developers need is SQL,” Hu said, echoing the growing realization across the industry — even amongst early NoSQL innovators such Google — that SQL stays essential for constructing 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 right into a distributed context without rewriting code.
This becomes critical within the AI era. AI agents, especially those operating autonomously at the sting 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 brand new infrastructure stress test
One of the notable features 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 are available waves and have to be handled elastically,” Hu said. “These agents must also meet data residency requirements — and all of this must be always-on.”
Oracle is positioning its system as ideally suited to this class of workload. The mixture of Raft-based active-active replication, multi-region availability and policy-based data distribution allows customers to administer high availability, scalability and compliance in a single coherent platform.
Raft replication is used to make sure consistency across distributed nodes. In Oracle’s implementation, it allows for sub-three-second failover with zero data loss — even within 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 pressing challenges for global enterprises is managing compliance with a growing patchwork of information localization laws. Countries equivalent to India, China and EU member states are increasingly enforcing policies that restrict where data can reside and the way it could be processed.
Oracle’s system addresses this through automated, policy-driven data placement. Customers can use predefined policies to be certain that data stays inside specific geographies — even while applications operate across regions. As Hu explained, one major U.S. bank is using the system to store data individually in India and the U.S. while presenting a unified database view to applications running within the U.S.
This enables Oracle to support global operations without requiring enterprises to copy full infrastructure stacks in every country — a cost-prohibitive proposition in smaller or emerging markets. As a substitute, Exascale allows customers to spin up lightweight deployments in specific jurisdictions, scaling resources elastically as needed.
This model guarantees to dramatically reduce the barriers to compliance and makes it economically viable for organizations to increase operations into recent markets.
Automation, not only 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 are able to do all of this for you,” Hu noted, stating that organizations don’t need to administer distribution manually unless they wish to. “The applying doesn’t know where the information is positioned. It’s completely transparent.”
This strikes at a critical pain point for a lot of enterprises, particularly those with limited in-house database expertise. Developers and IT teams don’t wish to be database administrators. They wish to give attention to constructing value. Oracle’s promise is to abstract away the complexity without compromising control.
AI, meet your data
Perhaps probably the most strategically vital 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 information.
“Most corporations store their business data in Oracle,” Hu said. “We wish you to do AI together 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 inside the data layer.
This convergence simplifies architecture, reduces ETL overhead and ensures data security and compliance. It also signifies that AI workloads profit from the identical enterprise-grade replication, availability and observability as transactional applications.
Economics of exascale
All of this might be less compelling if the economics didn’t pencil out. But Oracle says it’s targeting price sensitivity with its Exascale model. Customers can start with as little as 40 CPUs and 200GB of storage and scale elastically based on demand.
“Exascale permits you to start really small,” Hu explained. “In case you’re establishing a presence in a brand new country, you’ll be able to put a minimal footprint there and grow because the business scales.”
By separating compute and storage and offering serverless provisioning, Oracle is reducing the capital commitment required to arise global data infrastructure — while preserving the total fidelity of its enterprise-grade database.
Strategic outlook: Is that 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’s late to cloud or too wedded to legacy models. The success and momentum of Oracle Cloud Infrastructure (OCI) has gone a great distance in addressing these criticisms. This release may mark one other proof point. Oracle shouldn’t be just adapting to the brand new era, it’s applying a brand 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 ought to be within 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 provide rigor over novelty.
When asked for customer proof points, Oracle provided the next to theCUBE:
“Providing exceptional customer satisfaction is significant to PayPal, so we’ve been using Oracle Exadata for a few years to offer 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 offer even faster responses by utilizing distributed solutions which can be integrated with our core systems of record to offer extreme availability and performance. We look ahead to using Oracle Globally Distributed Exadata Database on Exascale Infrastructure’s always-on, serverless architecture with built-in Raft replication to speed up 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 take into consideration AI and data at global scale. The product signals Oracle’s ambition to be greater than a legacy stalwart within the cloud era; it positions the corporate as a serious, forward-looking contender within the battle to define the longer term of intelligent infrastructure.
For enterprise architects tasked with constructing globally compliant, always-on, AI-infused data systems, Oracle’s recent platform deserves a spot on the shortlist. The mixture of maturity, modernity and mission-critical readiness makes it a serious alternative to each cloud-native upstarts and hyperscale incumbents.
With more details expected at Oracle CloudWorld in October, the industry shall be watching closely to see if Oracle can execute on its vision and whether the market is prepared to fulfill it there.
Stay tuned for the total video interview.
Photo: SiliconANGLE
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