What problems have you encountered when working with processing large data volumes in practice?

When working with large data volumes in practice, I often encounter several problems:

1. Memory and performance limitations — loading large files entirely into memory can lead to exhaustion. The solution is to use streaming processing or batch data loading.

2. Long processing times — large volumes require algorithm optimization, parallelization, or the use of specialized libraries (e.g., NumPy, Pandas with chunking).

3. Storage and data transfer issues — large files are difficult to store and transfer; sometimes compression or distributed file systems are necessary.

4. Errors and gaps in data — with large volumes, it is more challenging to control data quality, so automation of validation and cleaning is important.

An example solution in Python for reading a large CSV in parts:

import pandas as pd

chunksize = 10 ** 6  # read by a million rows
for chunk in pd.read_csv('large_file.csv', chunksize=chunksize):
    process(chunk)  # data processing function

This approach allows working with files that exceed available RAM.