Why OceanBase Built Lakebase: The Need for an AI‑Ready Lake‑Database

On June 29, OceanBase announced the launch of its AI‑focused database family, with Lakebase as the central engine that brings together lake and database capabilities to manage, process, retrieve and invoke structured, unstructured and vector data within a single architecture.

Traditional databases have long handled only structured data—tables that support transactions, orders, accounts, and finance. As AI technologies mature, text, images, audio, video and other multimodal data have become core assets that need to be understood, analyzed and leveraged, making the classic separation of data lake, database and data warehouse inadequate for unified AI workloads.

Lakebase’s core logic is straightforward: give multimodal data the same robust management capabilities as structured data, including unified metadata, indexing, compute and search, so that enterprises can efficiently use the massive “sleeping” data assets for AI applications.

The architecture combines the openness of a data lake—essential for handling massive, diverse, open‑format data in AI scenarios—with the strong governance, stability, permission control and reliable services of a database. It supports multiple compute engines (SQL, Spark, Daft) and hybrid search methods (keyword, vector, structured filters), allowing developers, data scientists and business analysts to work with data in the way that best fits their workloads.

Lakebase offers two deployment modes. Independent deployment is suited for brand‑new AI use cases, allowing a quick, resource‑light rollout of a full stack that includes storage and compute. Intelligent overlay mode lets customers reuse existing storage and data assets, connecting to heterogeneous sources and presenting a consistent access interface while preserving existing investments.

In autonomous‑driving scenarios, companies collect massive video, image, sensor and GPS data. Lakebase transforms this raw data into searchable assets by supporting video splitting, event slicing, key‑frame extraction, scene recognition and vectorization, then combines vector search, structured queries and multimodal search so that engineers can rapidly locate valuable samples, reducing data preparation cost and accelerating model iteration.

For securities firms, which already possess rich structured data (market, trades, finance) and unstructured data (research reports, announcements, regulatory documents, news), Lakebase acts as a processing hub. It parses reports, extracts titles, abstracts, tags, industry, securities information and indexes the content, enabling intelligent retrieval, compliance Q&A and downstream analytics.

The article concludes that a pure transactional database cannot meet multimodal data processing needs, nor can a pure data lake satisfy enterprise‑grade management, search and service requirements. The AI era demands a new lake‑database foundation that can manage both structured logic and unstructured semantics, serving both human users and AI agents as a stable, callable data infrastructure.