Software Alternatives, Accelerators & Startups

Pyramid Analytics VS Data Science Workbench

Compare Pyramid Analytics VS Data Science Workbench 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.

Data Science Workbench logo Data Science Workbench

Equip data scientists with self-service access to any data, anywhere, so they can quickly develop and prototype machine learning projects and easily deploy them to production.
  • 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.

  • Data Science Workbench Landing page
    Landing page //
    2023-10-05

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

Data Science Workbench features and specs

  • Collaborative Environment
    Cloudera Data Science Workbench provides a collaborative environment where data scientists can work together on projects, facilitating better communication and teamwork.
  • Scalability
    The platform supports distributed computing, allowing data scientists to scale their computations effortlessly using the underlying Cloudera cluster resources.
  • Language Flexibility
    It supports Python, R, and Scala, providing flexibility for data scientists who prefer different programming languages for their analyses and model development.
  • Security
    It offers robust security features, including authentication, authorization, and encryption, ensuring that data and model access is well-controlled and compliant with enterprise standards.
  • Ease of Setup
    The workbench is known for its ease of setup and integration within existing Cloudera environments, reducing the time to start projects.

Possible disadvantages of Data Science Workbench

  • Resource Intensive
    Running Cloudera Data Science Workbench can be resource-intensive, requiring significant computational power and memory, which may not be optimal for smaller setups.
  • Complexity of Full Utilization
    Utilizing the full range of features may require a steep learning curve and expert knowledge, which can be challenging for new users.
  • Cost
    It can be costly, especially for small and medium-sized enterprises, due to licensing fees and the need for a robust infrastructure to support it.
  • Limited Offline Capabilities
    The tool is largely dependent on a stable internet connection and might not support all use cases where offline capabilities are needed.
  • Dependency on Cloudera Ecosystem
    Optimal usage of the workbench is heavily reliant on integration with other Cloudera ecosystem products, which may not be ideal for users not fully invested in Cloudera's stack.

Pyramid Analytics videos

Data Science & AI Overview

More videos:

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

Data Science Workbench videos

Model Deployment Using Cloudera Data Science Workbench

Category Popularity

0-100% (relative to Pyramid Analytics and Data Science Workbench)
Data Dashboard
92 92%
8% 8
Business & Commerce
79 79%
21% 21
Technical Computing
0 0%
100% 100
Business Intelligence
100 100%
0% 0

Questions and Answers

As answered by people managing Pyramid Analytics and Data Science Workbench.

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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What are some alternatives?

When comparing Pyramid Analytics and Data Science Workbench, you can also consider the following products

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

Tibco Data Science - Data science is a team sport. Data scientists, citizen data scientists, business users, and developers need flexible and extensible tools that promote collaboration, automation, and...

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

IBM ILOG CPLEX Optimization Studio - IBM ILOG CPLEX Optimization Studio is an easy-to-use, affordable data analytics solution for businesses of all sizes who want to optimize their operations.

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.

AIXON - AIXON is an AI-powered data science solution that enables data scientists of all levels of experience to build machine learning models and deploy them into production with less code and without the need for a data science team.