Ultimately, analysts build models in order to predict how a security, a complex trade, or an entire market will perform in the future. A key input in this analysis is the historic price of an asset, and it is not uncommon to utilize 20 or more years worth of pricing data with a model. For example, the daily data collected for a specific equity is typically the open, high, low, and closing price, along with the trading volume of the equity. Managing time series data has posed a unique problem to financial technologists. Relational models are inefficient containers for time series, and provide neither high performance nor a useful programming interface.
Data sets containing daily data are typically several gigabytes in size.
In a typical implementation, the data sets are stored on a server and delivered to a client, where it is analyzed. The task of integrating various types of data has been left to individual customers and consulting firms. The newest trend among quantitative analysts is the desire to analyze transactional, or tick data, rather than daily data. For example, an analyst studying equities now wants a data set containing the price and volume for each and every trade for a particular stock over a number of years. A server-centric model is required.