Best data store for billions of rows -- If you mean 'Engine', then InnoDB. How big can a MySQL database get before the performance starts to degrade -- Again, that depends on the queries. I can show you a 1K row table that will meltdown; I have …

Apr 26, 2017 · Reading the data in chunks allows you to access a part of the data in-memory, and you can apply preprocessing on your data and preserve the processed data rather than raw data. It'd be much better if you combine this option with the first one, dtypes. I want to point out the pandas cookbook sections for that process, where you can find it here ...

On the other hand, plotting-big-data is a pretty common task, and there are tools that are up for the job. Paraview is my personal favourite, and VisIt is another one. They both are mainly for 3D data, but Paraview in particular does 2d as well, and is very interactive (and even has a Python scripting interface).

When we talk about Big Data, we talk in most cases about huge amount of data that in many cases is constantly written. Data can have a lot of variety as well. Think of a typical data source for Big Data as a machine in a production line that produces all the time sensor data on temperature, humidity, etc.

Oct 11, 2013 · Don't mix: Large data to update vs. large application. EF is very handy for every-day data operations, i.e. displaying, editing, adding single records. It is not optimized to do bulk insert/update operations but there are discussions and attempts and solutions to that. See this SO Q&A for more information. –

Apr 5, 2012 · You can create views or indexed views on subset of data you are interested in and run queries on view -- which should contain only interesting subset of data much smaller than the whole table. Pros: Easy to do. It's up to date with source data. Cons: Possible only for defined subset of data. Could be inefficient for large tables with high rate ...

Sep 19, 2012 · Normalize. This can actually have some big drawbacks for performance, but it can have some performance advantages and it has big data consistency advantages as well as storage advantages. Consider your datatypes. If you need NVarchar, you need NVarchar. But if varchar will work, then it will use up less space. Same with int vs bigint. Partition.

short big = 0xdead; short little = (((big & 0xff)<<8) | ((big & 0xff00)>>8)); You could also declare a vector of unsigned chars, memcpy the input value into it, reverse the bytes into another vector and memcpy the bytes out, but that'll take orders of magnitude longer than bit-twiddling, especially with 64-bit values.

Feb 23, 2016 · When a big boss believes this is a big opportunity because data is the new oil and gold, and get a big pile of money to throw out a window and flush it down the bowels. And then your data warehouses and silos turn into a data lake and the data lake full of synergy into a data swamp full of bit rot; where the big vision hits the reality that not ...

Jun 17, 2016 · The database should have at least 6-8 tables with lots of foreign keys in between them, i.e a complete database. The MySQL employees database looked promising, but the download page has 3 download links, clicking on any of which opens a page in a browser with a god-awful amount of binary data, dont know what to do with that.