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NumPy VS Atlan

Compare NumPy VS Atlan and see what are their differences

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Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Atlan logo Atlan

Atlan is an advanced data workspace developed to offer benefits to many different sources of data.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Atlan Landing page
    Landing page //
    2023-08-23

NumPy

Website
numpy.org
$ Details
Release Date
-
Edit details

Atlan

Website
atlan.com
$ Details
-
Release Date
2018 January
Startup details
Country
Singapore
State
Central Region
City
Singapore
Founder(s)
Prukalpa Sankar
Employees
50 - 99
Edit details

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.

Atlan features and specs

  • Collaboration
    Atlan provides a collaborative platform for data teams, allowing users to manage and share data assets, which can enhance teamwork and productivity.
  • Integration
    The tool integrates with a variety of data sources and services, enabling users to easily connect and manage diverse data assets in one place.
  • User-Friendly Interface
    Atlan features an intuitive and user-friendly interface, making it accessible to users of varying technical skills, from data engineers to business analysts.
  • Data Governance
    Atlan offers robust data governance features, such as lineage and metadata management, which help organizations maintain data quality and compliance.
  • Automation
    The platform allows for automation of repetitive tasks, which can save time and reduce errors in data management processes.

Possible disadvantages of Atlan

  • Complexity for Small Teams
    While feature-rich, Atlan might be too complex for smaller teams or projects that do not require comprehensive data management capabilities.
  • Cost
    Atlan's pricing may be a concern for smaller organizations or startups, as advanced features can come at a significant cost.
  • Customization Limitations
    Some users might find the customization options limited compared to other data management platforms, which could impact specific use-case implementations.
  • Learning Curve
    New users may experience a steep learning curve when starting with Atlan due to the extensive range of features and capabilities it offers.

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.

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

Atlan videos

+ Add

3-Minute Atlan Demo

More videos:

  • Review - ATLAN SAFE OUTDOORS Tent Chair Blind Review Vs. AMERISTEP Tent Chair Blind (WHATS BETTER??)
  • Review - Atlan: Leveraging Founder-market Fit to Build a Global SaaS Brand

Category Popularity

0-100% (relative to NumPy and Atlan)

NumPy

Atlan

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Business & Commerce
0 0%
Business & Commerce
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Office & Productivity
0 0%
Office & Productivity
100% 100

User comments

Share your experience with using NumPy and Atlan. For example, how are they different and which one is better?
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Reviews

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

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

Atlan Reviews

We have no reviews of Atlan yet.
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Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than Atlan. While we know about 122 links to NumPy, we've tracked only 2 mentions of Atlan. 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.

NumPy mentions (122)

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Atlan mentions (2)

  • Thoughts around decube.io (data observability and catalog platform)
    After evaluating few solutions in the market: We were in the market to hunt for a solution which will cost under 10k (yearly) considering the cost of opensource will be similar considering DE resource and maintenance cost etc 1. MonteCarlo - Super duper expensive - Unable to hosting in Google Cloud 2. BigEye - Good features 3. Metaplane - Overall good package but when compared to catalog and other features it... Source: over 3 years ago
  • Data lake observability
    I've previously built data lakes on AWS with Glue and you get the data catalog for free but it isn't convenient to explore. Enterprise-grade data catalogs such as Alation are full featured and really decent but come at a higher cost. If your preference is open source, check out Atlan and Amundsen. Source: over 3 years ago

What are some alternatives?

When comparing NumPy and Atlan, 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.

Minitab Connect - Minitab Connect is a data management platform that comes with cloud-based data and integration workflows having data governance and integration tools.

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

Microsoft Azure Purview - Microsoft Azure Purview is a unified data governance solution that provides capabilities that cover the entire lifecycle from ingestion to cleansing, transformation, and security.

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

Kylo - Kylo is an end-to-end data lake management software that provides data from many sources in an automated fashion and optimizes it.

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