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Aerospike VS NumPy

Compare Aerospike VS NumPy and see what are their differences

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Aerospike logo Aerospike

Aerospike is a high-performing NoSQL database supporting high transaction volumes with low latency.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Aerospike Landing page
    Landing page //
    2023-09-16
  • NumPy Landing page
    Landing page //
    2023-05-13

Aerospike features and specs

  • High Performance
    Aerospike is designed to provide low-latency data access even at high throughput levels, making it suitable for real-time applications.
  • Scalability
    The database scales efficiently across multiple nodes, allowing it to handle large data volumes while maintaining performance.
  • ACID Compliance
    Aerospike provides ACID properties at the record level, ensuring data consistency and reliability in transactions.
  • Hybrid Storage
    Supports both in-memory and persistent storage, enabling efficient use of resources based on application needs.
  • Strong Consistency
    Offers strong consistency models that ensure operations are viewed consistently, which is critical for certain applications.

Possible disadvantages of Aerospike

  • Complexity
    Setting up and configuring Aerospike can be complex, requiring specialized knowledge, especially for optimization.
  • Cost
    While Aerospike offers a community edition, the enterprise version can be costly, potentially impacting decisions for small organizations.
  • Limited Query Capabilities
    Compared to some NoSQL databases, Aerospike has more limited querying features, focusing on key-value and secondary index lookups.
  • Community Support
    Although the community around Aerospike is growing, it may not be as large or active as those of some other database systems.
  • Complex Data Modeling
    The key-value data model can require significant adaptation for complex data that might be more naturally represented in relational databases.

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.

Aerospike videos

+ Add

Aerospike Demo of Aggregation Querying

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 Aerospike and NumPy)

Aerospike

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

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Reviews

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

Aerospike Reviews

7 Best NoSQL APIs
The last piece of the puzzle when it comes to the attraction of Aerospike is its hybrid memory architecture. Aerospike takes an approach to storing data uniquely. It stores the index only in memory while the data persists in a solid state drive (SSD). While the magic in output lies deeper in the architecture, clients receive sub-millisecond latency read times at a throughput...
Source: www.programmableweb.com
When to use Aerospike vs Redis | Aerospike
Need for strong data consistency If companies are building mission-critical applications where data consistency is a must, then Redis is not likely the right choice. Redis has not passed the Jepsen test for strong consistency (whereas Aerospike has). Redis supports eventual consistency, which can result in stale reads and even data loss under certain circumstances. Redis has...
Source: www.aerospike.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 Aerospike. While we know about 119 links to NumPy, we've tracked only 8 mentions of Aerospike. 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.

Aerospike mentions (8)

  • Aerospike Driver for LINQPad
    Aerospike for LINQPad 7 is a data context dynamic driver for interactively querying and updating an Aerospike database using “LINQPad”. The driver is free. For more information go to this blog post. You can directly download the driver from the LINQPad NuGet manager. Source: about 2 years ago
  • Using In-Memory Databases in Data Science
    Aerospike is a real-time cloud structured platform with good performance capabilities. This IMDB platform allows enterprises to perform their operations in real time through the hybrid memory and parallelism model. - Source: dev.to / over 2 years ago
  • Block and Filesystem side-by-side with K8s and Aerospike
    Block storage stores a sequence of bytes in a fixed size block (page) on a storage device. Each block has a unique hash that references the address location of the specified block. Unlike a filesystem, block storage doesn't have the associated metadata such as format-type, owner, date, etc. Also, block storage doesn’t use the conventional storage paths to access data like a filesystem file. This reduction in... - Source: dev.to / over 2 years ago
  • Aerospike & IoT using MQTT
    This example shows how the Aerospike database can be easily and scalably used to store industrial time series data made available by the MQTT ecosystem. Aerospike plus its Community Time Series Client streamlines the storage and retrieval of the data, supporting the ability to both write and read millions of data points per second if required. - Source: dev.to / over 2 years ago
  • Building Large-Scale Real-Time JSON Applications
    Real-time large-scale JSON applications need reliably fast access to data, high ingest rates, powerful queries, rich document functionality, scalability with no practical limit, always-on operation, and integration with streaming and analytical platforms. They need all this at low cost. The Aerospike Real-time Data Platform provides all this functionality, making it a good choice for building such applications.... - Source: dev.to / over 2 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 / 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 Aerospike 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.

memcached - High-performance, distributed memory object caching system

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

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

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

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