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Dask VS DHTMLX

Compare Dask VS DHTMLX and see what are their differences

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LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Dask logo Dask

Dask natively scales Python Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love

DHTMLX logo DHTMLX

JavaScript Library for cross-platform web and mobile app development with HTML5 JavaScript widgets. Easy integration with popular JavaScript Frameworks.
  • Dask Landing page
    Landing page //
    2022-08-26
  • DHTMLX Landing page
    Landing page //
    2023-07-27

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.

DHTMLX features and specs

  • Comprehensive Suite
    DHTMLX offers a wide range of UI components, from grids and charts to complex Gantt and scheduler components, providing a comprehensive toolkit for web application development.
  • Rich Documentation
    The platform provides extensive documentation, demos, and examples, which are invaluable for developers in understanding and implementing various components efficiently.
  • Cross-Browser Compatibility
    DHTMLX components are designed to be fully compatible across major browsers, ensuring a consistent user experience regardless of the user's environment.
  • Support and Community
    DHTMLX offers various support options including forums and ticket-based support, alongside a strong user community that can provide insights and assistance.
  • Customizability
    The components are highly customizable, allowing developers to tailor them to fit the specific aesthetics and functionality requirements of their projects.

Possible disadvantages of DHTMLX

  • Cost
    While DHTMLX provides a free version, the full suite with advanced features requires a paid license, which can be a drawback for startups or individual developers with limited budgets.
  • Complexity
    With its comprehensive set of features, DHTMLX can have a steep learning curve for newcomers who are unfamiliar with its architecture and functionalities.
  • Dependency on Proprietary Framework
    Using DHTMLX can lead to a dependency on its particular frameworks and methodologies, which might be a concern for projects that prioritize open-source solutions.
  • Performance Overhead
    Implementing multiple DHTMLX components in a single application might introduce performance overhead, which requires optimization to maintain responsiveness.
  • Limited Open Source Contributions
    As a commercial library, DHTMLX might not benefit from as many open source contributions and innovations as purely open-source alternatives.

Dask videos

+ Add

DASK and Apache SparkGurpreet Singh Microsoft Corporation

More videos:

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

DHTMLX videos

+ Add

Dhtmlx Scheduler

More videos:

  • Review - dhtmlxGantt 6.1 Release: Time Constraints, Backward Scheduling, S-curve and dataProcessor Update
  • Review - Dhtmlx-Grid with Flux4 Part2

Category Popularity

0-100% (relative to Dask and DHTMLX)

Dask

DHTMLX

Workflows
100 100%
Workflows
0% 0
Javascript UI Libraries
0 0%
Javascript UI Libraries
100% 100
Databases
100 100%
Databases
0% 0
Development Tools
0 0%
Development Tools
100% 100

User comments

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Reviews

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

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

DHTMLX Reviews

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

Social recommendations and mentions

Based on our record, Dask seems to be a lot more popular than DHTMLX. While we know about 16 links to Dask, we've tracked only 1 mention of DHTMLX. 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.

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 / almost 4 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

DHTMLX mentions (1)

  • How Much Does It Cost To a Project Management App
    To finish projects on time, efficient time management is a must. To help managers deal with deadlines, we usually implement a fully functional Gantt chart. To create a reliable and user-friendly UI, choosing the right set of tools is essential. In our work, we usually rely on Webix, a JavaScript UI library developed by the brightest minds of XB Software. Another ace up our sleeve is JavaScript UI libraries by... - Source: dev.to / over 3 years ago

What are some alternatives?

When comparing Dask and DHTMLX, 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.

Bryntum - High performance web components for SaaS apps - including Gantt, Scheduler, Grid, Calendar and Kanban widgets. Seamless integration with React, Vue, Angular or plain JS apps.

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

Sencha Ext JS - Sencha Ext JS is the most comprehensive JavaScript framework for building data-intensive, cross-platform web and mobile applications for any modern device. Ext JS includes 140+ pre-integrated and tested high-performance UI components.

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

Webix UI - An enterprise JavaScript Library for cross-platform app development with HTML5 JavaScript widgets and easy integration with most popular JavaScript Frameworks.

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