Online or onsite, instructor-led live kdb+ training courses demonstrate through interactive hands-on practice the fundamentals of kdb+.
kdb+ training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Madrid onsite live kdb+ trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Madrid - Chamartín Business Area
NobleProg Madrid, Street Bausá 27, Madrid, spain, 28033
Madrid is a municipality and city of Spain is the most populated city of the country, with 3 165 235 inhabitants. On the econ...
Madrid is a municipality and city of Spain is the most populated city of the country, with 3 165 235 inhabitants. On the economic level, it is the fourth richest city in Europe, after London, Paris and Moscow. It is an influential cultural center and has museums of international reference, among which stand out the Prado Museum, the National Museum of Art Reina Sofia, Thyssen-Bornemisza and CaixaForum Madrid.
Being one of the largest cities in all of Spain and one of the most populated, it is characterized by having an extensive transport network consisting of rail, metro and bus lines, in addition to its airport.
Located in the business area of Chanmartin five minutes from Plaza Castilla and less than ten minutes from the airport Madrid Barajas and Madrid downtown area.
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
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