Software Alternatives, Accelerators & Startups

Pyramid Analytics VS NumPy

Compare Pyramid Analytics VS NumPy and see what are their differences

Pyramid Analytics logo Pyramid Analytics

Pyramid brings data prep, business analytics, and data science together into one frictionless business and decision intelligence platform that helps you deliver timely and effective decision-making.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Pyramid Analytics Landing page
    Landing page //
    2024-09-04

Pyramid is an enterprise-grade Decision Intelligence Platform designed to seamlessly scale from individual self-service analytics to large-scale deployments. It supports a wide range of capabilities from basic data visualizations to advanced machine learning, catering to diverse user needs. The platform features a universal client for any device and operating system, facilitating installation on various platforms including on-premises and cloud environments, and interoperability with popular data stacks.

Pyramid emphasizes a balance between self-service productivity and governance, serving as an adaptive analytic platform that adjusts capabilities based on user skills. It manages content as a shared resource, supporting organizations throughout their decision workflows and bridging the gap between analytics strategy and implementation.

The Analytics OS includes six core modules (Model, Formulate, Discover, Illustrate, Present, and Publish) alongside administrative and content management tools, providing a comprehensive analytics experience across the workflow.

Pyramid Analytics, headquartered in Amsterdam with global offices, offers the Pyramid Decision Intelligence Platform. This AI-enhanced solution integrates data preparation, business analytics, and data science to simplify data-driven decision-making. It enables direct data operation without extraction, promoting self-service and governance while supporting complex BI needs.

The platform ensures rapid data-to-decision cycles with a no-code, AI-driven approach, supporting direct access to multiple data sources and environments. It facilitates interactive analysis, data visualization, and machine learning for predictive insights. Pyramid's platform is deployable across cloud, on-premises, or hybrid environments, empowering users with AI-guided workflows and natural language interfaces for intuitive analytics.

  • NumPy Landing page
    Landing page //
    2023-05-13

Pyramid Analytics

$ Details
paid Free Trial
Platforms
MacOS Android Windows Android
Release Date
2016 January
Startup details
Country
Netherlands
Founder(s)
Omri Kohl, Avi Perez, Herbert Ochtman
Employees
100 - 249

Pyramid Analytics features and specs

  • Visualizations
    Create a wide variety of charts and graphs to effectively communicate data stories
  • Drill-Down & Slicing/Dicing
    Analyze data from different angles and uncover hidden patterns in real-time
  • Data Blending
    Combine data from various sources seamlessly for a holistic view
  • Interactive Dashboards
    Design dynamic dashboards to share insights and track key performance indicators (KPIs)
  • Pre-Built Connectors
    Connect to a wide range of data sources easily, including cloud applications and databases
  • Custom Connectors
    Build custom connectors for unique data sources for maximum flexibility
  • Data Security
    Ensure data protection with features like encryption, user authentication, and role-based access control (RBAC)
  • Natural Language Processing (NLP)
    Interact with data using natural language for more intuitive analysis
  • Embedded Analytics
    Embed reports and visualizations into internal applications for seamless data access
  • White-Labeling
    Customize the platform's look and feel to match your brand. Scalability: Supports large and complex datasets for enterprise-level needs
  • AI-Powered Insights
    Get automated data insights and recommendations to uncover hidden patterns and accelerate decision-making

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.

Pyramid Analytics videos

Data Science & AI Overview

More videos:

  • Demo - Business Analytics Overview
  • Demo - Data Preparation Overview
  • Demo - The Decision Intelligence Platform Overview

NumPy videos

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 Pyramid Analytics and NumPy)
Data Dashboard
72 72%
28% 28
Data Science And Machine Learning
Business & Commerce
100 100%
0% 0
Data Science Tools
0 0%
100% 100

Questions and Answers

As answered by people managing Pyramid Analytics and NumPy.

Who are some of the biggest customers of your product?

Pyramid Analytics's answer

Hallmark Empyrean Premier Foods

What makes your product unique?

Pyramid Analytics's answer

Pyramid Analytics is unique due to its unified platform combining data preparation, business analytics, and data science with AI-driven self-service. It offers scalability, performance, strong governance, and a user-friendly experience.

Why should a person choose your product over its competitors?

Pyramid Analytics's answer

Pyramid Analytics stands out with its unified platform, AI-driven insights, and ability to handle complex data, empowering users of all skill levels to make informed decisions faster than with other tools.

How would you describe your primary audience?

Pyramid Analytics's answer

Pyramid Analytics targets data-driven organizations seeking a comprehensive, user-friendly platform to unlock insights from complex data, empowering both business users and data analysts to collaborate effectively.

What's the story behind your product?

Pyramid Analytics's answer

Pyramid Analytics emerged from a need for a more intuitive and powerful business intelligence solution. It was founded on the principle of democratizing data, enabling organizations to harness the full potential of their data through a unified, AI-driven platform.

Which are the primary technologies used for building your product?

Pyramid Analytics's answer

Pyramid Analytics is built on a robust technology stack including:

  • Core: C#, .NET, JavaScript
  • Data Engine: In-memory OLAP, SQL, MDX
  • AI and Machine Learning: Python, R, TensorFlow, PyTorch
  • Cloud Infrastructure: AWS, Azure, GCP
  • Frontend: HTML5, CSS3, React

User comments

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Reviews

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

Pyramid Analytics Reviews

We have no reviews of Pyramid Analytics yet.
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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.
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 more popular. 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.

Pyramid Analytics mentions (0)

We have not tracked any mentions of Pyramid Analytics yet. Tracking of Pyramid Analytics recommendations started around Mar 2021.

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 Pyramid Analytics and NumPy, you can also consider the following products

QlikSense - A business discovery platform that delivers self-service business intelligence capabilities

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

Owler - Owler is a crowdsourced data model allowing users to follow, track, and research companies.

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

Foxmetrics - We track the interactions of your customers with your web or mobile applications in real-time, and provide actionable metrics that will help increase your conversion.

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