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Harness VS NumPy

Compare Harness VS NumPy and see what are their differences

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Harness logo Harness

Automated Tests For Your Web App

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Harness Landing page
    Landing page //
    2023-07-24
  • NumPy Landing page
    Landing page //
    2023-05-13

Harness

Website
harness.io
Pricing URL
Official Harness Pricing
$ Details
-
Release Date
2016 January
Startup details
Country
United States
State
California
City
San Francisco
Founder(s)
Jyoti Bansal
Employees
250 - 499
Edit details

NumPy

Website
numpy.org
Pricing URL
-
$ Details
Release Date
-
Edit details

Harness features and specs

  • Continuous Delivery Automation
    Harness provides robust continuous delivery automation, allowing teams to automate deployment processes, reduce errors, and improve the speed of releasing software.
  • Finance Efficiency
    Harness includes a cost management feature that helps organizations control cloud infrastructure costs by providing insights and optimizing resource usage.
  • Simplified CI/CD Pipelines
    It offers simplified CI/CD pipeline creation with an easy-to-use interface, which reduces the complexity and time required to set up these processes.
  • Advanced Security Features
    Integrates advanced security features, such as vulnerability scanning and role-based access control, ensuring that deployments meet security compliance requirements.
  • Smart Rollbacks
    Harness has the capability of smart rollbacks that enable teams to automatically revert to a stable version if a new deployment faces issues, minimizing downtime.

Possible disadvantages of Harness

  • Learning Curve
    There can be a significant learning curve for teams new to Harness, as understanding its full capabilities and integrating it into existing workflows can take time.
  • Cost
    Harness might represent a higher cost than some other CI/CD tools, which can be a concern for smaller businesses or teams with tighter budgets.
  • Complex Configurations
    Some users report that setting up complex configurations can require advanced technical expertise, potentially necessitating additional training or hiring.
  • Integration Limitations
    Although Harness integrates with various tools, there can be limitations or complexities in configuring and maintaining these integrations, especially with less common tools.
  • Dependence on Internet Connectivity
    As a cloud-based solution, Harness requires reliable internet connectivity, which might pose challenges for teams with unstable internet connections or strict data residency requirements.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Harness videos

+ Add

How to FIT & CHOOSE a CLIMBING HARNESS w/ Black Diamond! VLOG

More videos:

  • Review - Best Climbing Harnesses - Top 7 Climbing Harness Reviews
  • Review - Best Dog Harness in 2019 - Top 5 Dog Harness Review

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to Harness and NumPy)

Harness

NumPy

DevOps Tools
100 100%
DevOps Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Cloud Computing
100 100%
Cloud Computing
0% 0
Data Science Tools
0 0%
Data Science 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 Harness and NumPy

Harness Reviews

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NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy should be more popular than Harness. It has been mentiond 119 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.

Harness mentions (15)

  • DevOps in 2025: the future is automated, git-ified, and kinda scary but fun.
    Harness — AI-powered delivery pipelines. - Source: dev.to / about 1 month ago
  • Best CI/CD for AWS services?
    Can check out our products at harness.io. Source: almost 2 years ago
  • How we moved from Artifactory and saved $200k p.a. Part 3 of 5 - The future is Advanced Artefacts
    Harness is our Continuous Delivery (CD) tool of choice. It provides a flexible template engine, that we were able to utilise to create templates that could be reused across our teams. - Source: dev.to / over 2 years ago
  • How to Install Drone CI Server in Kubernetes
    Drone by Harness is a continuous integration service that enables you to conveniently set up projects to automatically build, test, and deploy as you make changes to your code. Drone integrates seamlessly with Github, Bitbucket and Google Code as well as third party services such as Heroku, Dotcloud, Google AppEngine and more. - Source: dev.to / almost 3 years ago
  • Harness
    Does anyone have any opinion about the DevOps company Harness - harness.io? (they also have a defunct sub r/Harnessio/). How is the pay in India (Glassdoor and AmbitionBox gives very different figures). How is the work-life balance? In Glassdoor, it doesn't look good at all. If you are a current or ex-employee, would you advise rather to not join it? Source: almost 3 years ago
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NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 4 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 9 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 10 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 10 months ago
View more

What are some alternatives?

When comparing Harness and NumPy, you can also consider the following products

Deployment.io - Deployment.io makes it super easy for startups and agile engineering teams to automate application deployments on AWS cloud.

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

Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

GitHub Actions - Automate your workflow from idea to production

OpenCV - OpenCV is the world's biggest computer vision library

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