ZenithDB Stream Engine
ZenithDB Stream Engine is a simple, powerful, and cost-efficient stream processing platform built on Proton that unifies streaming and historical data analytics, enabling enterprises to gain real-time insights and drive business loops using standard SQL.
KEY FEATURES
Real-time stream processing with unified analytics, built for extreme performance.
Unified Streaming and Historical Processing
Features a unique dual-layer architecture with a Write Ahead Log (WAL) and Historical Store, allowing users to seamlessly query both real-time streams and massive historical data using standard SQL, eliminating architectural redundancy.
Extreme Performance and Latency
Built with C++ and optimized using SIMD instructions, it delivers up to 90 million events per second (EPS) and end-to-end latency as low as 4 milliseconds on a single node.
Lightweight and Effortless Deployment
The entire engine is a single binary under 500MB with no dependencies like JVM or ZooKeeper, supporting rapid deployment from edge devices to cloud clusters.
Powerful Streaming SQL Capabilities
Fully supports ANSI-SQL with built-in Tumble/Hop/Session windows, streaming joins, and complex event processing (CEP) capabilities, lowering the barrier for intelligent application development.
High-Performance Mutable Streams
Native support for UPSERT and DELETE operations, purpose-built for complex scenarios such as real-time materialized caches, dynamic dimension table lookups, and GDPR compliance.
Full-Stack AI and Custom Extensions
Supports User-Defined Functions (UDFs) in Python and JavaScript, enabling the direct integration of machine learning models into streaming pipelines for deep intelligence.
Extensive Ecosystem Connectivity
Includes native connectors for Kafka, Redpanda, Pulsar, ClickHouse, and S3, with support for over 200 data sources through integrated Redpanda Connect.
USE CASES
Powering real-time intelligence across industries with unified stream analytics.
Real-Time Fraud Detection
Monitor transactions in real-time to identify and prevent fraudulent activities using advanced AI patterns.
IoT Device Monitoring
Process streaming sensor data from IoT devices to monitor equipment status and trigger alerts.
Real-Time Log and System Monitoring
Process and analyze large volumes of platform logs, operational metrics, and event data generated by digital services.
Real-Time User Behavior Analytics
Capture and analyze live user interaction streams to deliver personalized experiences and optimize conversion funnels instantly.