Lakebase, the core engine of OceanBase AI Database, unifies storage, management, computation and search for structured, unstructured and vector data, offering multimodal data capabilities, flexible deployment modes, and AI‑oriented use cases such as autonomous driving and financial document analytics.
The article argues that as AI agents replace human users, the real bottleneck for enterprise AI shifts from model performance to data infrastructure, and explains how OceanBase’s AI‑native database—Lakebase—addresses multimodal data, hybrid search, agent safety, and massive logical tables to enable production‑grade AI applications.
Loop Engineering shifts AI from single prompts to continuous feedback loops, and by capturing human perception‑decision‑action‑feedback cycles with multimodal signals, the new Ego‑NeuroLoop paradigm promises far more data‑efficient embodied intelligence than existing ego‑centric video datasets.
The Flink Forward Asia 2026 conference in Shenzhen showcased Apache Flink's evolution to Agentic Streaming for AI, introduced the multimodal Agentic Lake built on Apache Paimon 2.0, announced Fluss 1.0 as a real‑time context layer, and highlighted performance gains over competing stacks such as Ray and Daft.
The article analyzes how enterprise‑level AI agents, when deployed in production, struggle with factual data retrieval despite semantic capabilities, and argues that real‑time, low‑latency, multimodal analytics—exemplified by systems like Apache Doris and SelectDB—are the essential data entry points for successful Agent deployments.
The article examines how Alibaba Cloud OSS Vector Bucket solves the data‑scattered, costly, and inefficient retrieval problems of massive multimodal media asset platforms by unifying storage, providing semantic vector search, and cutting operational expenses up to 95%.
This article details Hisense's journey to create an AI‑ready multimodal data platform, covering the challenges of integrating diverse business systems, the shift from a Hadoop‑based architecture to a cloud‑native data lake, the JuData governance and development platform, and six practical scenarios that demonstrate unified ingestion, metadata management, rule‑based quality control, intelligent asset retrieval, and future AI‑driven DataOps capabilities.
Lance File Format v2.2 tackles the AI data explosion by delivering hundred‑fold random‑read performance, advanced two‑layer compression, zero‑cost schema evolution, Git‑style versioning, external blob handling, and a roadmap toward native media support and intelligent encoding, positioning it as a core infrastructure for large‑scale multimodal workloads.
The article explains how Alibaba Cloud's MaxCompute has been transformed into a cloud‑native Data+AI platform, detailing its layered architecture, multimodal storage, model management, hybrid compute scheduling, SQL AI functions, the MaxFrame Python framework, and several enterprise case studies that demonstrate performance gains and flexible resource orchestration.
In the AI era, data consumption moves from a few technical users to all business staff, forcing a fundamental redesign of data governance across five dimensions—resource consumption, frequency, semantics, knowledge base, and modality—and proposing three actionable strategies to make data semantically rich, fully multimodal, and AI‑consumable.
Paxini, together with JD Cloud, Tencent Cloud, and Baidu Intelligent Cloud, launches the world’s first hundred‑billion‑scale, full‑modal, high‑degree‑of‑freedom embodied AI data cloud mall, offering instant online data procurement, end‑to‑end model training pipelines, and validated performance gains in both lab and real‑world robot tasks.
This article analyzes how the Daft‑Ray‑Lance stack tackles the challenges of multimodal AI workloads by offering a high‑performance Rust engine, adaptive back‑pressure, seamless Ray‑based distributed scheduling, and a storage format optimized for random access, vector indexing, and zero‑copy schema evolution, complete with benchmark comparisons and practical deployment guidance.
This article examines how large‑language models can reconstruct the data‑warehouse DWD layer by automating ETL script generation, data cleaning, standardization, and cross‑table association, presenting three high‑frequency scenarios—structured data cleaning, multimodal data parsing, and intelligent table linking—along with tool selections, step‑by‑step procedures, real‑world case studies, and practical pitfalls.
Haier’s digital transformation leverages a cloud‑native, open‑source‑based multi‑modal data lake that unifies structured and unstructured industrial data, uses metadata models and knowledge graphs for governance, and provides AI‑ready services that balance performance, cost, and real‑time requirements.
The article recaps the Apache Hudi Asia Meetup hosted by JD, covering community updates, JD's data‑lake challenges, the upcoming Hudi 1.1 release, JD's architectural redesign, Kuaishou's real‑time lake adoption, and Huawei Cloud's deep optimizations, all aimed at building an AI‑native, real‑time lakehouse.
This article series explores Tencent Cloud's Iceberg‑based batch‑stream integration, Apache Gravitino's unified metadata and lineage solution, Xiaohongshu's data‑architecture evolution for the Big AI Data era, and a practical Data+AI multimodal data‑lake implementation, highlighting challenges, architectural designs, and performance gains.
This article collection examines Tencent Cloud’s Iceberg batch‑stream integration, AI‑driven game data governance, Apache Gravitino unified metadata and lineage, Xiaohongshu’s multimodal data‑lake evolution, and Volcano Engine’s Data+AI multimodal lake, highlighting architectures, techniques, performance gains, and practical implementations.
This guide walks through constructing an AI‑powered multimodal data analysis platform on Hologres 4.0, covering PDF ingestion, vector and full‑text indexing, AI functions, stored procedures, and hybrid retrieval queries to enable enterprise‑level search and insight extraction.
Game‑TARS, a general‑purpose game AI from ByteDance's Seed team, replaces custom function calls with low‑level keyboard‑mouse actions, leverages massive multimodal data, sparse‑thinking and decaying‑loss algorithms, and achieves zero‑shot mastery across diverse games, surpassing top large models like GPT‑5 and Gemini‑2.5‑Pro.
The article discusses the challenges of training ever‑larger AI models on internet‑scale data, critiques traditional batch ETL pipelines, and proposes a streaming data‑flow architecture with dynamic data selection and a shared‑memory/Alluxio middle layer to decouple data processing from model training, improving efficiency and scalability.
The talk reviews the rapid evolution of data technologies—from early database foundations and big‑data breakthroughs to the rise of generative AI—highlighting how Ant Group’s data platform is shifting from a cost‑efficiency focus to a value‑centric, multimodal, AI‑driven ecosystem.
The article explains how AI databases combine structured, unstructured, and vector data, integrate machine‑learning, NLP, and generative models, and why platforms like Vespa are emerging as open‑source solutions to meet the performance and scalability demands of modern generative AI applications.
This article reviews the rising importance of data quality for large models, explores data‑centric AI, large‑model pre‑training data engineering, and presents recent Fudan University research on using large models to improve data governance across multiple domains such as attribute normalization, geographic cleaning, compliance checking, and multimodal retrieval.
This presentation details how Wing Payment leverages a large‑scale, multimodal knowledge graph and AI techniques—including computer vision, unsupervised and supervised learning, federated learning, and graph neural networks—to detect and mitigate fraud across payment, e‑commerce, and credit scenarios, while outlining system architecture, algorithmic approaches, case studies, and future research directions.
The article presents a comprehensive anti‑fraud framework that analyzes black‑market fraud characteristics, reviews conventional fraud‑mitigation methods, and proposes a multimodal graph‑neural‑network approach—leveraging device, behavior, and content similarity—to accurately identify fraudulent users on large‑scale internet platforms.
