Ant Data leverages Ant Group's leading technological innovation in big data, databases, and multimedia, with years of industry practice. Through long-term technology planning and continuous innovation, we strive to build world-class data technology and products.
This article shares the practical lessons learned from building Dataphin‑MCP, an AI‑enabled data‑development platform, by outlining five concrete "silver" rules, illustrating each with real‑world cases, and discussing deeper considerations for building robust AI‑first tools and harnesses.
This article walks through turning a generic AI model into a personal assistant by explaining user‑centric workflows, crafting effective natural‑language prompts, adding clarification steps, validating AI‑generated results through multiple methods, and handling errors with product interactions to create a reliable, evolving assistant.
This article explains the challenges of high‑dimensional vector retrieval, introduces quantization techniques—especially the binary RaBitQ method and its MRQ extension—detailing their compression ratios, speed gains, compatibility with search indexes, and real‑world performance results in the VSAG system.
The article presents an in‑depth overview of DeepInsight Copilot, an AI‑driven business intelligence product that streamlines data, information, insight, and decision‑recommendation stages, detailing its functional modules, intelligent agents, multi‑generation technical evolution, architecture, model fine‑tuning, and future challenges and solutions in data analysis.
The article examines the rapid growth of unstructured data, explains the fundamentals and resource‑intensive nature of vector retrieval, presents Ant Group’s engineering practices—including hybrid HNSW‑DiskANN indexing, performance tricks like BSA pruning and memory prefetching, sparse‑vector and feedback‑driven recall improvements—and outlines the open‑source VSAG roadmap and ecosystem integrations.
Ray Flow Insight is an Ant Group open‑source tool that visualizes Ray's distributed programming primitives—Actors, Tasks, and Objects—to turn complex reinforcement‑learning systems from opaque "black boxes" into transparent, debuggable workflows, providing logical, physical, distributed stack, and flame‑graph views for performance analysis and optimization.
This article presents a comprehensive exploration of using Apache Paimon and Flink to design lake tables that support minute‑level latency, low cost, and unified batch‑stream processing for advertising data, covering schema design, partitioning strategies, performance trade‑offs, cost analysis, and operational best practices.
This article provides an in‑depth technical analysis of DeepSeek's open‑source 3FS distributed file system, focusing on the StorageService architecture, space pooling, allocation mechanisms, reference counting, fragmentation handling, and the RDMA‑based read/write data path.
This article examines the challenges of AI storage for massive models, describes Ant Group's multi‑cloud, high‑availability PCache architecture, and details its GPU‑mixed deployment, metadata services, data‑link optimizations, and performance results that enable petabyte‑scale training with low cost and high stability.
This article provides an in‑depth analysis of DeepSeek's open‑source 3FS distributed storage system, focusing on its network communication module, RDMA‑based design, core classes such as IBSocket, Listener, and IOWorker, and how these innovations advance high‑performance AI infrastructure.
