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Ceph VS NumPy

Compare Ceph VS NumPy and see what are their differences

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Contents:
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  3. » Reviews
  4. » Alternatives

Ceph logo Ceph

Ceph is a distributed object store and file system designed to provide excellent performance...

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Ceph Landing page
    Landing page //
    2022-04-16
  • NumPy Landing page
    Landing page //
    2023-05-13

Ceph features and specs

  • Scalability
    Ceph is designed to scale horizontally by adding more nodes. This allows for seamless expansion of storage capacity as needs grow.
  • High Availability
    Ceph provides high availability and fault tolerance through its distributed architecture and data replication methods, ensuring data is always accessible.
  • Open Source
    Being an open-source project, Ceph has a large community of developers and users which help in rapid identification and rectification of issues. It also offers lower cost of ownership compared to proprietary solutions.
  • Versatility
    Ceph supports block storage, object storage, and file systems within the same cluster, providing great flexibility and reducing the need for multiple storage solutions.
  • Performance
    Ceph delivers high performance, particularly for large-scale deployments, by balancing loads and efficiently distributing data.

Possible disadvantages of Ceph

  • Complexity
    Setting up and maintaining a Ceph cluster can be complex and requires skilled administrators, which might not be suitable for smaller organizations.
  • Resource Intensive
    Ceph can be resource-heavy, demanding significant CPU, memory, and network resources, which can be a limitation for smaller setups.
  • Documentation
    Despite a rich set of features, Ceph’s documentation can sometimes be lacking or difficult for new users to comprehend, potentially leading to longer learning curves.
  • Hardware Requirements
    Ceph typically requires high-quality, enterprise-grade hardware to achieve optimal performance and reliability, which can entail a higher upfront investment.
  • Operational Overhead
    Day-to-day management, monitoring, and troubleshooting of Ceph clusters require a specialized skill set, leading to possible increases in operational overhead.

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.

Ceph videos

+ Add

UDS 2013-03: Ceph Review - Part 1/2

More videos:

  • Review - Designing for High Performance Ceph at Scale
  • Review - RHCS 4 Cockpit Ceph Installer

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 Ceph and NumPy)

Ceph

NumPy

Cloud Storage
100 100%
Cloud Storage
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Storage
100 100%
Storage
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 Ceph and NumPy

Ceph Reviews

Simplyblock as alternative to Ceph: A Comprehensive Comparison
Ceph utilizes its own storage driver (rbd) that is integrated into the Linux Kernel and can also be used on other platforms as a third-party driver. It enables seamless connectivity between hosts and the Ceph cluster. In addition to OpenStack, Ceph offers deep integrations with Kubernetes through a separate CSI driver, as well as other platforms.
Source: www.simplyblock.io
Best & Cheapest Object Storage Providers With S-3 Support
The libraries of Ceph support applications built in Java, C, C++, PHP, Python, and other languages. It also gives these apps access to its object storage platform via a native API.
Source: macpost.net
What are the alternatives to S3?
Ceph is a software-defined storage platform that implements object storage. Its interface is built with the same storage system that provides the librados interface, making it have the same abilities as librados like read-only snapshot and revert to snapshot. The software delivers Object, File, and Block storage in a single, unified system. Ceph is S3 compatible, and its...
Source: www.w6d.io
Ceph Storage Platform Alternatives in 2022
Open-Source software platforms are not free but you can use them as community edition or with limited features. The above storage platforms have same goals but also have some different abilities and capabilities, so choosing or using them is depended to your requirements and budget. About Ceph, I think that Ceph is still the best and there is no limitation for community...
Source: www.teimouri.net
15 FreeNAS Alternatives 2020 | Best Storage Operating System
PetaSAN is a Ceph-based iSCSI cluster, open-source FreeNAS alternative, known widely for its end-to-end integrated solution and scale-out SAN arrangement that offers impressive adaptability and execution. Its latest cloud storage technology makes it corporate-efficient to manage large data storage in one unit; run on the Linux operating system, the program has many nodes...
Source: solutionsuggest.com

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 seems to be a lot more popular than Ceph. While we know about 119 links to NumPy, we've tracked only 11 mentions of Ceph. 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.

Ceph mentions (11)

  • 10 open source tools that platform, SRE and DevOps engineers should consider in 2024.
    Ceph stands out in storage technology, offering a scalable and reliable solution where traditional systems fall short. It supports object, block, and file storage in one system, adaptable for various environments including on-premises, cloud, or container-native setups. Key benefits include scalability, enabled by the CRUSH algorithm, allowing for expansion without typical downtime. This makes Ceph suitable for... - Source: dev.to / over 1 year ago
  • iSCSI over WAN / backup of remote site
    With that being said, you better take a look at something more WAN optimized and more secure, like S3 storage. You can build the S3 storage (and gain immutability) using something like MinIO (https://min.io/) or Ceph (https://ceph.io/en/) or check out Object First Ootbi offerings - https://objectfirst.com/object-storage/ (I work for them). Source: almost 2 years ago
  • What's the best AWS S3 protocol alternative?
    I believe Ceph [1] could be a good alternative. It can be self hosted and I believe some cloud providers also offer it. Here are some differences between S3 and Ceph [2]. [1] - https://ceph.io/en/ [2] - https://www.lightbitslabs.com/blog/ceph-storage/. - Source: Hacker News / almost 2 years ago
  • Seeking Advice & Opinions: Hybrid NAS/Cloud Storage for Family Use
    Another option is a distributed Ceph cluster https://ceph.io/en/. Source: over 2 years ago
  • First Time NAS buyer for Digital Textile Printing Factory
    There's also cool systems like https://ceph.io/en/ that could be efficient if willing to set up and learn. Source: almost 3 years ago
View more

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 / 8 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 / 8 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 / 9 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 / 9 months ago
View more

What are some alternatives?

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

Minio - Minio is an open-source minimal cloud storage server.

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

GlusterFS - GlusterFS is a scale-out network-attached storage file system.

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

StorPool - StorPool is designed from the ground up to provide cloud builders, shared hosting providers and MSPs with the most resource efficient storage software on the market.

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

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