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PySpark VS Dask

Compare PySpark VS Dask and see what are their differences

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PySpark logo PySpark

PySpark Tutorial - Apache Spark is written in Scala programming language. To support Python with Spark, Apache Spark community released a tool, PySpark. Using PySpark, you can wor

Dask logo Dask

Dask natively scales Python Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love
  • PySpark Landing page
    Landing page //
    2023-08-27
  • Dask Landing page
    Landing page //
    2022-08-26

PySpark features and specs

No features have been listed yet.

Dask features and specs

  • Parallel Computing
    Dask allows you to write parallel, distributed computing applications with task scheduling, enabling efficient use of computational resources for processing large datasets.
  • Scale
    It scales from a single machine to a large cluster, providing flexibility to develop code locally on a laptop and then deploy to cloud or other high-performance environments.
  • Integration with Existing Ecosystem
    Dask integrates well with popular Python libraries like NumPy, pandas, and Scikit-learn, allowing users to leverage existing code and skills while scaling to larger datasets.
  • Flexibility
    Dask can handle both data parallel and task parallel workloads, giving developers the freedom to implement various algorithms and solutions efficiently.
  • Dynamic Task Scheduling
    Dask's dynamic task scheduler optimizes the execution of tasks based on available resources, reducing malfunction risks and improving resource utilization.

Possible disadvantages of Dask

  • Complexity in Setup
    Setting up Dask, particularly in distributed settings, can be complex and may require significant infrastructure management efforts.
  • Performance Overhead
    While Dask provides high-level abstractions for parallel computing, there can be performance overhead due to its abstractions and scheduling mechanics which might not match the performance of highly optimized, low-level code.
  • Limited Support for Some Libraries
    Dask's smart parallelization might not perfectly support all features of libraries like pandas or NumPy, potentially requiring workarounds.
  • Learning Curve
    Despite its integration with Python's data science stack, Dask presents a learning curve for those unfamiliar with parallel computing concepts.
  • Debugging Challenges
    Debugging parallel computations can be more challenging compared to single-threaded applications, and users need to understand the distributed computation model.

PySpark videos

+ Add

Data Wrangling with PySpark for Data Scientists Who Know Pandas - Andrew Ray

More videos:

  • Tutorial - Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Training | Edureka

Dask videos

+ Add

DASK and Apache SparkGurpreet Singh Microsoft Corporation

More videos:

  • Review - VLOGTOBER : dask kitchen review ,groceries ,drinks
  • Review - Dask Futures: Introduction

Category Popularity

0-100% (relative to PySpark and Dask)

PySpark

Dask

Databases
36 36%
Databases
64% 64
Workflows
0 0%
Workflows
100% 100
Project Management
100 100%
Project Management
0% 0
Big Data
46 46%
Big Data
54% 54

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare PySpark and Dask

PySpark Reviews

We have no reviews of PySpark yet.
Be the first one to post

Dask Reviews

Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
Dask: You can use Dask for Parallel computing via task scheduling. It can also process continuous data streams. Again, this is part of the "Blaze Ecosystem."
Source: www.xplenty.com

Social recommendations and mentions

Based on our record, Dask seems to be more popular. It has been mentiond 16 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

PySpark mentions (0)

We have not tracked any mentions of PySpark yet. Tracking of PySpark recommendations started around Mar 2021.

Dask mentions (16)

  • Large Scale Hydrology: Geocomputational tools that you use
    We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk. Source: over 3 years ago
  • msgspec - a fast & friendly JSON/MessagePack library
    I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec. Source: over 3 years ago
  • What does it mean to scale your python powered pipeline?
    Dask: Distributed data frames, machine learning and more. - Source: dev.to / over 3 years ago
  • Data pipelines with Luigi
    To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:. - Source: dev.to / almost 4 years ago
  • How to load 85.6 GB of XML data into a dataframe
    I’m quite sure dask helps and has a pandas like api though will use disk and not just RAM. Source: almost 4 years ago
View more

What are some alternatives?

When comparing PySpark and Dask, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

NumPy - NumPy is the fundamental package for scientific computing with Python

SciPy - SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering. 

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Hadoop - Open-source software for reliable, scalable, distributed computing

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