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Backgrounds:

KDB+ is widely used in Financial industry and others. It is inmemory, column based, efficient especially procient tp process Financial data set. Many investbanks, hedged funds and proptrading hours emploited KDB+ to many data analytics and data services. KDB+ play a significant role in analysis in back testing and daily trading, find out root cause and improve trading quality and efficiency.?Python is also widely used in Finiancial industry and it can manipulte KDB+ easily, provide many libs to do analysis.

In this course, will introduce how Q/KDB+/Python are used in Financial industries(how to store data, how is the data API used, how is gateway exploited to support concurrent connnections, trouble shooting and??support on KDB+ and etc) and many senarios and relevant solutions.

What's the advantage of KDB+ in financial analysis?

Senarios

Performance & Efficiency

which kind of financial dataset

KDB+ fundamentals

type definiation & cast

functional select/update/delete

functions/lamda, sync/async function invocation

web socket support

file compression

sym enumeration and denumeration

splay table and partition

How can we deploy KDB+

tickplant

RDB/HDB

gateway/API

Reporting

How can we access KDB+

Q

Python

R

Java

C/C++

How can import data from other data source into KDB+?

txt/csv

html/web page

SQL Server