1 views collected around this technical thread.
The article describes how GF Securities designed and implemented a unified big‑data empowerment layer based on Apache Kyuubi to address data‑centric challenges, improve efficiency, ensure controllable governance, and support agile data scenarios across ingestion, processing, storage, and security.
This article explains the design and implementation of a unified data security control platform that protects user privacy and corporate data across multiple big‑data components (Hive, Hetu, GaussDB) by integrating Apache Ranger, custom authorization APIs, asynchronous processing, distributed locking, and SDK‑based authentication to achieve fine‑grained, one‑stop permission management.
Bilibili’s data platform redesigns Ranger‑based access control by simplifying HDFS and Hive policy APIs, parallelizing policy loading, adding gray‑release and pre‑check mechanisms, integrating fine‑grained Hive authorization with data‑masking, extending support to Spark and Presto, and planning incremental loading, policy fusion, and a NameNode proxy to boost security and performance.
This article describes the evolution from an unsecured data warehouse to a comprehensive big‑data access control system at BanYu, detailing the background, data access methods, design goals, authentication and authorization mechanisms, policy configuration, integration with Metabase, and the overall workflow that balances security with efficiency.
Didi’s big‑data platform secures user access by adding a custom password‑based Hadoop authentication stored on the NameNode and managed through its Shumeng user‑management service, while enforcing column‑level, role‑based permissions via an Apache Ranger‑powered system that classifies data, generates policies, and now governs millions of assets.
The article outlines Youzan’s evolving security architecture for its big‑data platform—starting with Apache Ranger‑based permission control, moving to a centralized permission‑management service for easier requests and audits, and finally implementing column‑level masking via SQL rewriting to protect sensitive data while balancing usability.