DASK: A Guide to Process Large Datasets using Parallelization

DASK: A Guide to Process Large Datasets using Parallelization

DASK: A Guide to Process Large Datasets using Parallelization. A simple solution for data analytics for big data parallelizing computation in Numpy, Pandas, and Scikit-Learn Frameworks.

Introduction

If you are dealing with a large amount of data and you are worried that Pandas’ data frame is unable to load it or NumPy arrays get stuck in between and you even need a much better and parallelized solution for your data processing and training machine learning models then dask open up a solution to this problem. Before diving into that, let’s see what actually is dask?

Before diving-in deep, have you ever heard about Lazy-Loading? Check out how Vaex is dominating the market of loading huge datasets.

What is dask?

Dask is an extremely efficient open-source project that uses existing Python Apis and knowledge structures that makes it straightforward to modify between Numpy, Pandas, Scikit-learn into their Dask-powered equivalents. Also, Dask’s schedulers scale to thousand-node clusters and its algorithms are tested on** a **numberof themostimportant supercomputers withinthe world.

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Source: Scale up to clusters using Dask Parallelization

Installation

Does quality comes pre-installed inside your Anaconda but for pip you can get the complete one using this command:

Conda installation for Dask:

!conda install dask

pip installation for Dask:

!pip install “dask[complete]”

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