Databases 5 min read

DataFun Summit: Technical Papers on Graph Databases, Vector Databases, Real‑Time Data Warehouses and Industry Data Practices

The DataFun Summit page presents a collection of technical papers covering graph database parallel queries, next‑generation vector databases, real‑time data warehouse architectures, and best practices in finance and e‑commerce, while also providing instructions for obtaining the e‑book via a public account.

DataFunSummit
DataFunSummit
DataFunSummit
DataFun Summit: Technical Papers on Graph Databases, Vector Databases, Real‑Time Data Warehouses and Industry Data Practices

To receive the e‑book, click the tag below, follow the DataFunSummit public account, and send a private message containing "洞察力" to get the download instructions.

Exploration of Xiaohongshu Graph Database in Distributed Parallel Query – This paper introduces REDgraph, Xiaohongshu’s self‑developed graph database designed for massive social networks, detailing its distributed parallel query optimizations that significantly improve query efficiency and performance, and discusses its concepts, comparisons with relational databases, application scenarios, challenges, and solutions.

New Generation Vector Database DingoDB in the Era of Large Models – The article examines DingoDB’s multi‑modal vector database design and product advantages, highlighting its support for structured, semi‑structured, and unstructured data, high‑performance processing, and its applicability to business intelligence, data stream analysis, and other scenarios in the large‑model era.

New Practices of Tianqiong Data Warehouse Autonomy in the Era of Large Models – This piece shares Tencent’s Tianqiong big‑data autonomous platform’s latest practices, covering data governance background, autonomous capability construction, a dual‑engine implementation strategy, and future plans aimed at advancing data autonomy.

Application of Storage‑Based Real‑Time Data Warehouse Architecture in Douyin Group – The paper delves into how Douyin Group employs a storage‑centric real‑time data warehouse to meet massive data processing demands, analyzing its construction, data quality management, and service optimization strategies that enhance data‑driven decision‑making and user experience.

Financial‑Grade Real‑Time Data Warehouse Construction Practice – This article outlines Ant Group’s real‑time data warehouse architecture, real‑time data quality assurance, unified stream‑batch applications, and data lake implementation outlook, offering valuable insights for the financial sector.

Alibaba Cloud ClickHouse Enterprise Edition: Next‑Generation Cloud‑Native Serverless Real‑Time Data Warehouse – The piece introduces Alibaba Cloud’s ClickHouse Enterprise Edition, a cloud‑native serverless real‑time data warehouse built on open‑source ClickHouse, discussing its core features and elastic serverless capabilities for real‑time analytics.

Best Practices of Data Warehouse Construction and Data Governance in the Financial Industry – This article shares financial industry best practices for data warehouse building and governance, covering background, construction content, enterprise‑level data warehouse implementation, governance outcomes, and future planning.

58 User Profile Data Warehouse Construction Practice – The paper presents 58.com’s experience in building a user‑profile data warehouse, describing the overview of data warehouses and user profiling, the construction process, results, and summarizing how to create an efficient user data system.

To obtain the e‑book, follow the public account above and reply with "洞察力" to receive the electronic book download method.

Big DataReal-time Analyticsvector databasegraph databaseData Warehousedatabases
DataFunSummit
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DataFunSummit

Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.

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