How to deal with millions or rows of data for analysis/visualization purpose I have data in 2 tables in Sql server. 
First table has around 10 million rows and 8 columns. 
Second table has 6 million rows and 60 columns. 

I want to import those tables into a Python notebook using pandas ( I am importing in "chunksize")  and then merge them and then run analysis on resulting table. 

I am unable to import the data due to possible hardware constraints. 
System hardware configuration is as follows:

RAM: 4GB
Storage: 160 GB, 
CPU : Dual core CPU. 


Even if the import goes through (which seems difficult), my resulting table after the merge will have 5 million rows and 40 columns. 

Is it feasible to perform analysis/visualization in python notebook using pandas, seaborn on columns on the resulting table? 

Would love to understand: 

What should be my next steps to solve the problem? 

Is installation of Anaconda on Windows 2012 server feasible? (This server has more memory)
Pandas load everything into memory before it starts working and that is why your code is failing as you are running out of memory. One way to deal with this issue is to scale your system i.e. have more RAM but this is not a good solution as this method will eventually fail to scale.

Other option will be to use big data libraries like spark or flink for this on a distributed system. You can try the installation on windows sever but that also wont scale well for you.