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 introduces Flex, a Flink‑compatible stream‑batch vectorized engine built on Velox and Gluten, explains the SIMD‑based execution model, details native operator optimizations, fallback mechanisms, correctness and usability improvements, and presents performance results and future development plans.
This article surveys the evolution of modern data technologies—from the 4V characteristics of big data and the limitations of traditional relational databases, through the rise of NoSQL and polyglot persistence, to embedding‑driven vector search, hybrid retrieval and RAG, illustrating how each paradigm frees applications from data constraints.
This tutorial walks through the end‑to‑end process of creating a data‑assistant application using DB‑GPT V0.6.0, covering prerequisite deployment, knowledge‑base construction, sub‑agent creation, RAG‑based QA, AWEL workflow installation, intent‑recognition knowledge base, and unified multi‑agent orchestration.
Apache HoraeDB (CeresDB) is an open‑source, distributed, high‑availability time‑series database developed by Ant Group, supporting multi‑dimensional queries, compatible with Prometheus and OpenTSDB, and offering SQL and OLAP capabilities for use cases such as APM, IoT monitoring, financial analytics, and AI‑infra observability.
DB‑GPT is an open‑source AI‑native data‑application framework that provides multi‑model management, Text‑to‑SQL optimization, RAG, multi‑agent collaboration, and intelligent workflow orchestration, enabling developers to build scalable large‑model database applications, with proven enterprise adoption, community growth, and academic publications.
The article reviews the rapid advances in generative AI and big‑data technologies, traces the historical development of data infrastructure, and argues that modern data systems are shifting from a cost‑efficiency focus to a value‑centric paradigm driven by multimodal, non‑structured data, vector search and machine‑oriented services.
The 2024 Inclusion·Bund Conference forum brought together leading academics and industry experts to examine how data value is shifting in the AI era, covering large‑model storage challenges, the rise of synthetic data, AI‑enhanced databases, and Ant Group’s next‑generation intelligent data architecture.
This article reviews the latest vector database research showcased at SIGMOD 2024, covering system designs such as Starling, Vexless, RaBitQ, and ACORN, and discusses current academic hotspots including query processing, index structures, optimization techniques, and hardware acceleration for large‑scale similarity search.
This article examines Rockset’s cloud‑native real‑time analytics database, detailing its document‑oriented data model, RocksDB‑Cloud storage engine, compute‑storage separation, sharding, converged indexing, query processing pipeline, and the implications of OpenAI’s recent acquisition for the broader database ecosystem.
The article introduces LightPool, an open‑source cloud‑native storage‑pool architecture built on NVMe‑over‑Fabric that delivers high performance, low cost, and high availability for large‑scale distributed databases, and explains its design, scheduling, storage engine, and hot‑upgrade/migration capabilities presented at the 30th IEEE HPCA conference.
