ZenithDB for PostgreSQL
ZenithDB for PostgreSQL is a high-performance, cloud-native MPP database built on the Apache Cloudberry, specifically designed for enterprise-level massive data management, complex analytical workloads, and AI empowerment.
KEY FEATURES
Enterprise-grade MPP analytics with AI-native capabilities, built for scale.
Horizontal Scalability with MPP Architecture
Utilizes an advanced Shared-Nothing architecture to achieve near-linear growth in performance and storage capacity by distributing data and processing loads across multiple computing nodes (Segments).
Extreme Query Performance for Multiple Scenarios
Integrates the enhanced GPORCA optimizer, JIT (Just-In-Time) compilation, and Vectorized execution engine to significantly accelerate CPU-intensive analytical queries.
Lakehouse Integration and Data Federation
Supports cross-cluster federated queries and direct read/write access to mainstream data lake formats like Iceberg and Hudi on S3 or HDFS, enabling high-performance data federation access.
Full-Stack AI Enablement and Vector Similarity Search
Features built-in AIFun extensions and pgvector plugins, supporting seamless integration with mainstream LLMs to empower vector retrieval and intelligent application development.
Multi-modal Storage and High-Efficiency Compression
Supports multiple storage formats including row, column (AO), and PAX, while providing various compression algorithms such as Zstd and LZ4 to optimize storage costs and I/O efficiency.
Enterprise High Availability and Auto-Failover
Equipped with automatic failover capabilities for the Coordinator node and support for Kubernetes deployment, ensuring business continuity and multi-level fault tolerance.
High Ecosystem Compatibility
Deeply compatible with the PostgreSQL 14.4 ecosystem and Greenplum syntax, supporting standard SQL 2003 and Oracle compatibility extensions like Orafce.
USE CASES
Powering data-driven decisions across industries with massive-scale analytics.
Financial Risk & Transaction Analytics
Analyze massive transaction records, customer data, and risk indicators to support real-time financial analytics and regulatory reporting.
Customer Behavior & Sales Analytics
Analyze user interactions, orders, and product performance across multiple channels to gain deep insights into customer behavior.
Large-Scale Log & Operational Analytics
Process and analyze large volumes of platform logs, operational metrics, and event data generated by digital services.
AI Data Infrastructure & Vector Analytics
Provide scalable data infrastructure to support machine learning workloads and AI-driven applications.