Wei Hu
Senior Vice President of Research and Development
Palo Alto, California, United States
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Wei Hu is the Senior Vice President of Research and Development at Oracle. He leads the development of mission-critical database capabilities as well as Blockchain, Kubernetes, Microservices, and Globally Distributed Databases.
Mr. Hu has B.S. and M.S. degrees from the Massachusetts Institute of Technology (MIT). He has been with Oracle since 1998, holds more than 50 patents, and is the author and co-author of several books and papers.
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Scaling for Intelligence: Distributed SQL Databases in the Age of AI
As organizations race to integrate AI into their applications, the demand for systems that offer scalability, high availability, global distribution, and low latency has never been greater. Distributed SQL databases are emerging as a powerful solution, providing not only the performance required by modern, AI-driven workloads, but also the flexibility of SQL and strong consistency guarantees.
However, deploying these systems brings unique challenges. The familiar trade-offs between consistency, availability, and partition tolerance (the CAP theorem) still apply. Accessing data across geographies introduces network latency, and evolving data residency regulations now require thoughtful data placement and replication. Achieving rapid, automated recovery in the face of hardware, software, or network failures adds another layer of complexity—especially as AI and agent-based systems increase demands on reliability and performance.
In this talk, we’ll examine the inner workings of distributed databases, with a focus on best practices for designing applications that leverage their full capabilities—including for AI and agentic scenarios. Using Oracle Globally Distributed Database and other distributed SQL platforms, we’ll explore core architecture, advanced features, and the distinct application challenges of distributed data management. Patterns like sharding, data distribution, and parallel distributed transactions will be covered as practical ways to address latency and consistency—crucial for both traditional and AI-powered workloads.
To bring these concepts to life, we’ll share real-world case studies ranging from traditional applications to AI-driven and agent-based systems. By the end of the session, you’ll walk away with practical guidance and insights for designing scalable, resilient, and cloud-native solutions built for the next generation of AI-enabled workloads.
Modern Microservices for Enterprise AI: Patterns, Pitfalls, and Practices
AI is quickly becoming a must-have for businesses that want to stay ahead. To move faster, most teams are adding AI features to apps they already have. Microservices really help here—they break apps down into smaller, independent parts, which lets developers mix languages (like Java for the heavy lifting, and Python for AI and machine learning). This also makes it simpler to roll out and update new AI features as needs change.
In this session, we'll share what we've learned working with developers on real enterprise projects powered by AI. We’ll talk about practical topics like figuring out the right number of microservices, keeping up with fast-changing AI models, designing event-driven systems so agents can talk to each other, and managing workflows that blend traditional code with AI-driven components. We’ll also touch on smart ways to handle your data, so you get the right balance between keeping things separate and sharing what you need—while making sure everything stays consistent.
We'll go over design patterns that make it easier to build with AI—like how to organize services so you can trust AI-generated code, and tips for minimizing hallucinations. While we won’t have live demos during the session, we’ll share links to hands-on labs and code examples you can try afterwards. Our examples will use Spring, Java, and Oracle Database, but these ideas work across lots of tech stacks and programming languages.
Kubernetes and Microservices with Multi-Model Databases
Kubernetes and Microservices are important technologies for developing and deploying applications. In this talk, we will describe how a multi-model database such as Oracle is embracing and extending Kubernetes to enable developers to build mission-critical applications on these technologies. We will also describe how to best leverage the capabilities of a multi-model database such as Oracle to implement popular microservices patterns (such as Event Sourcing, Transactional Outbox, Idempotent, etc.). This talk will cover both what is in the current database release as well as a sneak peek at what is coming soon.
Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases
Distributed SQL Databases are a powerful new data management technology that addresses modern applications’ need for scalability, availability, geographic distribution, and low response time – with the power of SQL and strong consistency.
However, distributed databases bring their own challenges. For example, the familiar trade-offs between consistency, high availability, and partition tolerance still exist. Remote data access will still incur speed-of-light network delays.
This talk will describe the inner workings of distributed databases and how to design applications to best exploit their power. Using Oracle and other distributed databases as examples, we will explore the capabilities and unique application challenges posed by distributed databases. This talk will address design patterns that empower applications to handle network latency and consistency in the presence of real-world networks that can and do fail.
Crypto-secure Data Management with In-Database Blockchain
Existing security mechanisms are designed to keep hackers out. However, they have unavoidable vulnerabilities - chiefly due to human weaknesses (e.g., phishing attacks). We cannot prevent these break-in's, but we can minimize their impact by making critical data tamper-proof by using blockchain technologies.
Conventional blockchain systems, however, have been very difficult to use because of the requirement for new programming languages, tools, and workflow processes. This is changing as blockchain features are being incorporated in general-purpose databases. This makes it possible to implement blockchain in mainstream enterprise and government applications with minimal application changes.
This talk will begin by introducing the threats posed by hackers and compromised insiders. Then we will describe an implementation of in-database blockchain and how it can protect your data against these threats. We will compare this against conventional blockchains as well as share use cases from customers who have adopted this technology.
Wei Hu
Senior Vice President of Research and Development
Palo Alto, California, United States
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