This article compares Apache Sentry and Apache Ranger, two open‑source frameworks for fine‑grained, role‑based access control in Hadoop ecosystems, detailing their architectures, integration points, policy configuration, and practical examples of row‑level and column‑level security.
This article presents NetEase's practical experience of constructing a big‑data security center using Apache Ranger, covering Ranger's core features, a comprehensive security solution, detailed technical analyses, and the outcomes of commercializing the platform across multiple enterprise environments.
This article chronicles the step‑by‑step evolution of a data‑sandbox platform built on AWS EMR, detailing architectural changes from a simple S3‑backed 1.0 version through a Ranger‑secured 2.0 iteration to a cost‑optimized 3.0 design that balances security, performance, and usability for business analysts.
This article details the design and implementation of a comprehensive big data permission system that integrates Hive, Presto, Hadoop, and Metabase, covering data access methods, authentication choices, Ranger-based authorization, policy management, and automated workflow integration to balance security and efficiency.
This article describes the background, design goals, authentication and authorization mechanisms, system architecture, policy configuration, and Metabase integration of BanYu's big data permission system, highlighting how it balances security and efficiency across Hive, Presto, HDFS, and other components.
This article describes the background, design goals, authentication and authorization mechanisms, system architecture, policy configuration, and Metabase integration of Banyu's big data permission system, which secures Hive, Presto, HDFS and other data access components using Apache Ranger and LDAP.
This article explains Apache Ranger's role as a centralized security framework for Hadoop, detailing its key features, architecture, policy workflow, practical administration examples, and how to automate bulk policy management with Java and REST APIs.
This guide explains Apache Ranger’s role as a centralized security framework for Hadoop, detailing its core features, architecture, workflow, policy creation, auditing, field‑level masking, row‑level filtering, and how to automate policy management via its REST API and Java code.
The article evaluates Kerberos, Apache Sentry, and Apache Ranger for securing Meitu’s large‑scale Hadoop ecosystem, highlighting Ranger’s comprehensive, fine‑grained, policy‑based authorization across HDFS, HBase, Hive, YARN, Storm, and Kafka, and describing its configuration, LDAP integration, and custom SDK implementation.
