Insights

This paper examines how an AI-Native DB transforms data from mere storage into real-time operational assets. It presents business innovation strategies based on four key components of AI-Native DB: vector search, natural language query, embedded machine learning, and streaming data analytics.

Global Company H streamlined its complex multi-DBMS environment into an integrated, cloud-optimized architecture. This shortened the cloud transition timeline by 40%, reduced DB operation resources by 30%, and ensured stable migration of core systems.

Financial Group H addressed complexity and cost issues caused by diverse DBMSs by unifying heterogeneous DBMS environments into an open-source database, establishing a standardized operating system. As a result, they reduced total cost of ownership by 30% and enhanced IT efficiency and scalability through increased technological autonomy.

Leading Game Company K, which previously processed large-scale game logs through daily batch jobs, implemented a streaming platform to collect global server logs in real time and instantly detect abnormal behavior. This unified their operational solutions, reduced personnel resources, and strengthened fraud prevention and service stability.

Global semiconductor leader H needed to restructure the code system of key master data. To achieve this, they built a proactive architecture that predicts the impact of master data changes across the system and automatically restores data flow after modifications. As a result, operational lead time was reduced by 65%.

Leading insurance company H relied on manual reporting, which limited analysis speed and accuracy. To address this, a self-service data analytics environment was implemented. As a result, report generation time was reduced by over 70%, while the speed and accuracy of data-driven decision-making improved.

K Insurance, which lacked a web service log collection infrastructure, built a flexible integration architecture with an asynchronous messaging framework and streamed user behavior logs in real time to internal systems and external solutions. This improved the speed of identifying needs and service improvements based on user traffic analysis, greatly enhancing digital service quality.

Global manufacturing company S required uninterrupted data migration during equipment replacement and system upgrades. To address this, a fixed-schema CDC architecture was designed to enable real-time transaction replication between central and overseas data centers. As a result, S achieved zero-downtime operations during equipment changes and unified global production monitoring.

This paper analyzes the strategic value of open-source databases offering cost efficiency and technological flexibility, and examines key considerations for their adoption. It outlines the characteristics of major open-source databases such as PostgreSQL, MySQL, and MongoDB, and provides practical guidance for a successful transition.