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DolphinDB offers a one-stop big data solution. Our system integrates a high performance database, a lightweight distributed computing framework, and a fully-featured vector programming language. Huge volumes of data can be processed and analyzed at blazing-fast speeds. DolphinDB is also easy to learn, convenient to use, and fast to deploy.



DolphinDB’s performance exceeds the fastest available commercial big data systems, especially for commonly used SQL queries. It can process large scale datasets in milliseconds.


Data scientists can complete the entire process of big data analysis within a unified system, including data query, data clean, data modelling, and data visualization, etc. A fully-featured scripting language further speeds up the development. Special features of the language such as vector programming, functional programming, and built-in time series manipulation greatly reduce the amount of codes that users need to write.


There is no steep learning curve. Our language seamlessly integrates with most features of standard SQL. The syntax of our language is very similar to popular programming languages, such as Python and Java.


There is no need to go through complicated configuration steps as in Hadoop or Spark. Data scientists can finish the deployment of DolphinDB on multiple nodes in minutes, without the support of dedicated IT professionals. Implementation of ideas could be immediately executed in a distributed system without code compilation or deployment.



DolphinDB’s time series database is especially suitable for use in investment banks, quantitative hedge funds, and exchanges. It can be used to construct tests of strategies based on historical data, or to execute real time streaming tasks.


The lightweight yet highly efficient DolphinDB system can be applied to process and analyze historical and real-time data from industrial equipment sensors and enterprise systems, more efficiently and cost-effectively than any available alternatives.


DolphinDB’s distributed database can help telecommunication companies share data between users, towers and centers. Our data analysis system enables these firms to build better predictive usage models to allocate network resources more efficiently.