Using Containers for Big Data

on YouTube ↗01h 38m

" summary

Overview of Python data containers for big data storage and processing, comparing in-memory and on-disk options. Covers basic types (lists, dicts), NumPy ndarray, pandas DataFrame, and specialized storage like RDBMS, PyTables/HDF5 and bcolz (carray/ctable), with guidance on when to choose each based on use case.

AI-written summary · about

" related talks

• 34mSmall Big Data: using NumPy and Pandas when your data...Itamar Turner Trauring · PyData New York City 2019
• 01mSmall Big Data: using NumPy and PandasItamar Turner Trauring · Data Love 2021
• 26mSmall Big Data: using NumPy and Pandas when your data doesn't fit ...Itamar Turner Trauring · PyCon US 2020
• 42mData Science Meets ContainersSaulius Valatka · DevOps Pro Moscow 2016
• 25mNew Trends In Storing Large Data Silos With PythonFrancesc Alted · EuroPython 2015
• 01h 31mModern scientific computing and big data analytics in PythonEdward Schofield · PyCon Australia 2013
• 31mThe PyData map: Presenting a map of the landscape of PyData toolsPeadar Coyle · PyData Amsterdam 2016
• 35mData Warehousing with PythonMartin Loetzsch · code.talks commerce 2018