{"id":22707,"date":"2024-11-03T18:02:52","date_gmt":"2024-11-04T02:02:52","guid":{"rendered":"https:\/\/www.pingcap.com\/?p=22707"},"modified":"2026-01-05T06:59:35","modified_gmt":"2026-01-05T14:59:35","slug":"embracing-distributed-sql-to-drive-ai-readiness-in-banking-a-qa-with-insights","status":"publish","type":"post","link":"https:\/\/www.pingcap.com\/ko\/blog\/embracing-distributed-sql-to-drive-ai-readiness-in-banking-a-qa-with-insights\/","title":{"rendered":"Embracing Distributed SQL to Drive AI-Readiness in Banking: A Q&A from SACCC 2024"},"content":{"rendered":"

Financial services are rapidly transforming to adopt AI, digitize operations, and enhance customer experiences, yet many banks struggle with outdated legacy data systems<\/a> that limit AI adoption. <\/p>\n\n\n\n

At the Silverlake Axis Customer Connect Conference (SACCC), held from October 17 to 20, 2024, in Da Nang, Vietnam, industry leaders focused on modernizing banking infrastructure. C-suite executives, board members, and experts gathered to explore financial innovation, with sessions spotlighting the role of databases in achieving AI readiness. Experts from PingCAP and Silverlake Axis discussed how distributed SQL, particularly TiDB, can overcome legacy limitations, empower seamless scaling, and deliver real-time insights.<\/p>\n\n\n\n

During the session, the Silverlake Axis Chairman surprised the panel with a challenging question on how banks can address disparate requirements when modernizing their data architecture<\/a>, adding depth to the conversation on banking\u2019s AI-driven future.<\/p>\n\n\n\n

In this Q&A blog, we capture the key insights from Terry Purcell<\/a>, Chief Optimizer Architect at TiDB, who shared his expertise on how distributed SQL can unlock AI\u2019s potential in banking. Dr.Ashish Kakar<\/a>, IDC Financial Insights Asia\/Pacific Research Director, moderated the discussion.

In this blog, we’ve condensed the information into questions asked by Dr.Ashish and Terry Purcell’s answers in first person. <\/p>\n\n\n\n

<\/span>Why do legacy data systems pose a challenge for AI adoption in banking?<\/span><\/h2>\n\n\n\n

I believe legacy data systems pose a significant challenge to AI adoption in banking. I draw this perspective from an extensive background in database engineering. I spent 18 years working on mainframe Db2 development at IBM, where I led the query optimizer team. Over those years, I saw the strengths of these legacy systems but also their limitations. Systems like Db2 and Oracle are built on codebases that are around 45 years old. This age makes them inherently slow to innovate. They weren\u2019t designed to handle today\u2019s AI-driven environments, with their scale, speed, and integration demands.<\/p>\n\n\n\n

One major obstacle to AI adoption in banking is fragmentation within these legacy architectures. Many banks operate hundreds of disparate applications. This creates a highly disjointed data landscape. The result is data silos, where information is spread across multiple platforms. Each platform requires separate management and maintenance. This setup adds significant complexity and operational inefficiency. It becomes nearly impossible for banks to consolidate data effectively for real-time insights.<\/p>\n\n\n\n

My experience with Db2 has made me keenly aware of the need for a simpler, more adaptable data architecture. Over the past 12 years, distributed SQL has emerged as a critical innovation. It was developed from the ground up to address modern requirements. Unlike legacy databases, distributed SQL offers massive scalability and flexibility. It also simplifies the data landscape. With distributed SQL, banks can reduce the number of disparate systems. This move helps them shift toward a more streamlined, future-proof architecture.<\/p>\n\n\n\n

I believe that shifting to distributed SQL offers a path forward. This transition allows banks to overcome the limitations of their legacy systems. By adopting a distributed SQL architecture, financial institutions can consolidate their data infrastructure. They can reduce complexity and pave the way for real-time data processing. This shift ultimately enables AI-driven insights.<\/p>\n\n\n\n

<\/span>How can distributed SQL help banks overcome these challenges?<\/span><\/h2>\n\n\n\n

Distributed SQL offers a solution to the limitations of legacy systems, especially in banking, where stability and regulatory compliance<\/a> are crucial. Banks face more stringent requirements than other industries. Maintaining high availability and consistent data access is essential for them. Distributed SQL<\/a> helps manage these challenges more effectively.<\/p>\n\n\n\n

I\u2019ve seen firsthand how this approach works. A large U.S.-based social media company struggled with data fragmentation across disparate systems. Their developers had to manually piece together data from various sources, including OLAP systems, big data environments, AI models, and OLTP databases. The costs of this fragmentation were significant. They needed a unified solution to present a consistent data view to developers.<\/p>\n\n\n\n

Distributed SQL solved this issue by enabling horizontal scalability. It integrates different data workloads\u2014both transactional and analytical\u2014within one platform. This approach simplifies the data landscape, allowing banks to consolidate their infrastructure. Instead of relying on multiple systems, they can use one unified database architecture. For developers, this makes working with data more accessible and speeds up innovation.<\/p>\n\n\n\n

Interestingly, the demand for large-scale data integration and real-time processing often originates from non-banking sectors. Innovations driven by these industries are advancing distributed SQL. I\u2019m confident these advancements will benefit banks, helping them future-proof their systems. Distributed SQL addresses financial institutions’ needs by simplifying architecture, consolidating databases, and providing the scalability required for AI applications. This allows banks to streamline operations, gain more value from their data, and stay ahead of industry demands.<\/p>\n\n\n\n

<\/span>Are there real-world examples where distributed SQL has been successfully implemented in banking?<\/span><\/h2>\n\n\n\n

There are several significant use cases where distributed SQL, specifically TiDB, has proven valuable across the banking sector, particularly in Asia: <\/p>\n\n\n\n