2018年9月18日 · In this definition, 'big data' is data which, due to the particular challenges associated with the 4 V's, is unfit for processing with traditional database technologies; while 'big data tools' are tools which are specifically designed to deal with those challenges.

Big data, simply put, is an umbrella term used to describe large quantities of structured and unstructured data that are collected by large organizations. Typically, the amounts of data are too large to be processed through traditional means, so state-of-the-art solutions utilizing embedded AI, machine learning, or real-time analytics engines ...

2017年4月26日 · Is the file large due to repeated non-numeric data or unwanted columns? If so, you can sometimes see massive memory savings by reading in columns as categories and selecting required columns via pd.read_csv usecols parameter.

I don't understand how you get "Gigabytes" of data. 20 million x (3 x (4 bytes)) = 240MB, right? And @EOL is completely right -- converting all that perfectly good binary data into a text format is a complete waste of time and I/O, use numpy to access the binary directly.

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 …

2014年1月5日 · What is the difference between Big Endian and Little Endian Byte order ? Both of these seem to be related to Unicode and UTF16. Where exactly do we use this?

2016年11月28日 · Working with big data in python and numpy, not enough ram, how to save partial results on disc? I agree that h5py is useful but you may wish to use tools that are already in your quiver.

2012年9月19日 · Ditto other posters on how "large" depends what your data is, what kind of query you want to do, what your hardware is, and what your definition of a reason search time is. But here's one way to define "large": a "large" table is one that exceeds the amount of real memory the host can allocate to SQL Server.

2014年9月26日 · As the page needs more data such as going to a different page or searching, the server returns the client with more data. There are many ways to get your data into DataTables, and if you are working with seriously large databases, you might want to consider using the server-side options that DataTables provides.

As others have noted, when importing data into Postgres, things will be slowed by the checks that Postgres is designed to do for you. Also, you often need to manipulate the data in one way or another so that it's suitable for use. Any of this that can be done outside of the Postgres process will mean that you can import using the COPY protocol.