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Apache Ignite VS NumPy

Compare Apache Ignite VS NumPy and see what are their differences

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Apache Ignite logo Apache Ignite

high-performance, integrated and distributed in-memory platform for computing and transacting on...

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Apache Ignite Landing page
    Landing page //
    2023-07-08
  • NumPy Landing page
    Landing page //
    2023-05-13

Apache Ignite features and specs

  • In-Memory Data Grid
    Apache Ignite provides a robust in-memory data grid that can drastically improve data access speeds by storing data in memory across distributed nodes.
  • Scalability
    The system is designed to scale horizontally, allowing users to add more nodes to handle increased loads, thereby ensuring high availability and performance.
  • Distributed Compute Capabilities
    Ignite supports parallel execution of tasks across cluster nodes, which is beneficial for complex computations and real-time processing.
  • Persistence
    Although primarily in-memory, Ignite offers a durable and transactional Persistence layer that ensures data can be persisted on disk, providing a hybrid in-memory and persistent storage solution.
  • SQL Queries
    Ignite offers support for ANSI-99 SQL, which allows users to execute complex SQL queries across distributed datasets easily.
  • Integration
    It integrates well with existing Hadoop and Spark setups, allowing users to enhance their existing data pipelines with Ignite’s capabilities.
  • Fault Tolerance
    Apache Ignite includes built-in mechanisms for recovery and ensures that data copies are maintained across nodes for resilience against node failures.

Possible disadvantages of Apache Ignite

  • Complexity
    Apache Ignite can be complex to set up and manage, especially when configuring a large, distributed system with multiple nodes.
  • Resource Intensive
    Running an in-memory data grid like Ignite requires significant memory resources, which can increase operational costs.
  • Learning Curve
    Due to its comprehensive features and distributed nature, there is a steep learning curve associated with effectively utilizing Ignite.
  • Configuration Overhead
    There is substantial configuration overhead involved to optimize performance and ensure proper cluster management.
  • Community Support
    Although it has active development, the community support might not be as robust compared to other more mature solutions, possibly leading to challenges in finding solutions to niche issues.
  • YARN Dependence
    For those looking to integrate with Hadoop, Ignite's optimal performance is sometimes reliant on Hadoop YARN, which can introduce additional complexity.

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.

Apache Ignite videos

+ Add

Best Practices for a Microservices Architecture on Apache Ignite

More videos:

  • Review - Apache Ignite + GridGain powering up banks and financial institutions with distributed systems

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 Apache Ignite and NumPy)

Apache Ignite

NumPy

Databases
100 100%
Databases
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
NoSQL Databases
100 100%
NoSQL Databases
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Apache Ignite and NumPy. 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 Apache Ignite and NumPy

Apache Ignite Reviews

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

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 Apache Ignite. While we know about 119 links to NumPy, we've tracked only 3 mentions of Apache Ignite. 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.

Apache Ignite mentions (3)

  • API Caching: Techniques for Better Performance
    Apache Ignite — Free and open-source, Apache Ignite is a horizontally scalable key-value cache store system with a robust multi-model database that powers APIs to compute distributed data. Ignite provides a security system that can authenticate users' credentials on the server. It can also be used for system workload acceleration, real-time data processing, analytics, and as a graph-centric programming model. - Source: dev.to / 7 months ago
  • Ask HN: P2P Databases?
    Ignite works as you describe: https://ignite.apache.org/ I wouldn't really recommend this approach, I would think more in terms of subscriptions and topics and less of a 'database'. - Source: Hacker News / about 3 years ago
  • .NET and Apache Ignite: Testing Cache and SQL API features — Part I
    Last days, I started using Apache Ignite as a cache strategy for some applications. Apache Ignite is an open-source In-Memory Data Grid, distributed database, caching, and high-performance computing platform. Source: over 3 years ago

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 / 3 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 / 7 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 Apache Ignite and NumPy, you can also consider the following products

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

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

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

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

memcached - High-performance, distributed memory object caching system

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

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