About DolphinDB

• Established in 2016
• Ultra-fast, scalable, and easy-to-use platform for data storage, batch computing and stream computing
• Team: extensive Wall Street experience at Morgan Stanley, Barclays Capital, AllianceBernstein, etc.
• 200+ paying clients: banks, hedge funds, asset managers, exchanges, energy, manufacturing, etc.

Common Use Cases

• Market data center
• Quant research
• Algo trading
• Market making
• Post-trade analysis
• Risk management
• Metrics platform

Architecture

Features

• Multi-modal storage engines
• Time-series database: typically handles 20TB-10PB data
• In-memory OLTP for low latency high concurrency scenarios
• Primary key engine
• VectorDB
• TextDB
• Batch computing
• SQL engine
• Machine learning: dozens of built-in machine learning functions
• GPU support: DolphinDB script can run on GPU
• Stream computing
• Stream tables for publishing and subscription
• 10+ stream computing engines
• Unified stream and batch processing
• Complex Event Processing Engine
• Fully featured programming language
• Similar syntax as SQL and Python
• Support imperative, functional, vector, SQL and object-oriented programming
• Just-in-time compilation (JIT) significantly speeds up script execution
• Over 2000 built-in functions
• Advanced features for quant finance
• Research platform for collaborative strategy development (Starfish)
• Order book engine
• Simulated matching engine
• High frequency backtest engine

Enterprise-Grade Features

• High availability
• Horizontal scalability
• Separation of storage and compute
• Asynchronous replication
• Data catalog
• Single Sign-On

Visualization

Both PulseUI and QStudio support DolphinDB as well as 30+ other data sources.