Announcement
StarRocks - Fast Open Query Engine for Sub-second Analytics
The world's fastest open query engine for sub-second analytics both on and off the data lakehouse. With the flexibility to support nearly any scenario, StarRocks provides best-in-class performance for multi-dimensional analytics, real-time analytics, and ad-hoc queries. A Linux Foundation project.
Project Introduction
Summary
StarRocks is an open-source, high-performance analytical database designed for sub-second query latency across multi-dimensional, real-time, and ad-hoc analytics on diverse data landscapes, including data lakehouses.
Problem Solved
StarRocks addresses the need for extremely fast analytical querying across large datasets, overcoming the performance limitations and rigidity often found in traditional data warehousing or query-on-lake approaches.
Core Features
Sub-second Query Latency
Achieve query results in milliseconds across various data sources and scenarios.
Flexible Architecture
Supports a wide range of analytical use cases, including multi-dimensional, real-time, and ad-hoc queries, fitting into diverse architectural patterns.
Best-in-Class Performance
Optimized engine designed for high-performance analytical workloads.
Data Lakehouse Integration
Connects seamlessly with data stored on or off the data lakehouse.
Tech Stack
Use Cases
StarRocks' speed and flexibility make it suitable for a wide array of analytical use cases, enabling faster insights and more responsive applications.
Multi-dimensional Analytics (OLAP)
Details
Analyzing large datasets from various dimensions simultaneously to understand complex business trends and performance metrics.
User Value
Enable interactive exploration of data with rapid response times, facilitating deeper insights and faster decision-making.
Real-time Analytics
Details
Querying continuously arriving data streams or frequently updated datasets for immediate operational visibility and monitoring.
User Value
Monitor live events, track key metrics as they happen, and build responsive dashboards on fresh data.
Ad-hoc Querying
Details
Performing spontaneous, non-predefined queries on large datasets for data exploration, troubleshooting, or specific investigations.
User Value
Empower users to explore data freely without needing predefined aggregations or data models, accelerating discovery.
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