kdb+ and q: Analyze Time Series Data Training Course

kdb+ is an in-memory, column-oriented database and q is its built-in, interpreted vector-based language. In kdb+, tables are columns of vectors and q is used to perform operations on the table data as if it was a list. kdb+ and q are commonly used in high frequency trading and are popular with the major financial institutions, including Goldman Sachs, Morgan Stanley, Merrill Lynch, JP Morgan, etc.

In this instructor-led, live training, participants will learn how to create a time series data application using kdb+ and q.

By the end of this training, participants will be able to:

• Understand the difference between a row-oriented database and a column-oriented database
• Select data, write scripts and create functions to carry out advanced analytics
• Analyze time series data such as stock and commodity exchange data
• Use kdb+'s in-memory capabilities to store, analyze, process and retrieve large data sets at high speed
• Think of functions and data at a higher level than the standard function(arguments) approach common in non-vector languages
• Explore other time-sensitive applications for kdb+, including energy trading, telecommunications, sensor data, log data, and machine and network usage monitoring

Audience

• Developers
• Database engineers
• Data scientists
• Data analysts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice