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Camunda VS NumPy

Compare Camunda VS NumPy and see what are their differences

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Camunda logo Camunda

The Universal Process Orchestrator

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
Not present

The leader in process orchestration, Camunda enables organizations to operationalize and automate AI, integrating human tasks, existing and future systems without compromising security, governance, or innovation. Built for business and IT to collaborate, Camunda empowers organizations to overcome complexity, increase efficiency, and retain their competitive advantage no matter what speed and scale are required. Over 700 top organizations across all industries, including Atlassian, ING, and Vodafone trust Camunda with the design, orchestration, automation, and improvement of their business-critical processes to accelerate digital transformation. To learn more visit camunda.com

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

Camunda

$ Details
freemium
Release Date
2008 January
Startup details
Country
Germany
State
Berlin
City
Berlin
Founder(s)
Bernd Ruecker
Employees
250 - 499

Camunda features and specs

  • Agentic Orchestration
    Build AI agents, automate documents with AI-powered IDP, and run RPA bots.
  • Process Orchestration
    Coordinate the various moving parts and endpoints of a business process and tie multiple processes together for true end-to-end automation.
  • Scalability
    Camunda is designed to handle large-scale process automation, making it suitable for enterprise usage.
  • Rich API's
    REST and Java APIs that allow for seamless integration with other software systems and applications.
  • Open architecture
    Customize any workflow to fit your needs.
  • Marketplace
    Your hub for Camunda Accelerators like out-of-box connectors, process blueprints, and the ability to contribute to and request new solutions.
  • Common visual language
    Supports the BPMN 2.0 standard for process modeling, enabling you to adapt faster as business and processes evolves.
  • SAP Integration
    Automate processes that span your Systems of Record.
  • Flexible Deployment
    Cloud and self-hosting licensing available, giving organizations the flexibility to choose their preferred environment.
  • Cockpit and Tasklist
    Includes powerful tools like Cockpit for monitoring and Tasklist for task management, enhancing the control over process execution.
  • Community Support
    A large and active user community provides support, plugins, and shared knowledge, which can be very useful for troubleshooting and extending the platform.
  • Documentation and Training
    Comprehensive documentation and training materials help teams get up to speed quickly with the platform.

Possible disadvantages of Camunda

  • Steep Learning Curve
    Due to its extensive features and capabilities, the platform can be complex for new users to learn and master.
  • Licensing Costs for Enterprise Features
    While the open-source version is free, the enterprise edition with advanced features can be costly.
  • Customization Complexity
    Highly customizable but may require significant development effort to tailor it to specific business needs.
  • Resource Intensive
    Can be resource-intensive, requiring robust hardware to run efficiently, particularly for large-scale deployments.
  • Limited Out-of-the-Box Integrations
    Fewer built-in integrations compared to some competitors, often necessitating custom development work.

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.

Camunda videos

CamundaCon 2018: The Role of Workflows in Microservices (Camunda)

More videos:

  • Review - 5 Camunda advanced topics
  • Review - Camunda, The Universal Process Orchestrator

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 Camunda and NumPy)
BPM
100 100%
0% 0
Data Science And Machine Learning
Workflow Automation
100 100%
0% 0
Data Science Tools
0 0%
100% 100

Questions and Answers

As answered by people managing Camunda and NumPy.

Who are some of the biggest customers of your product?

Camunda's answer

likeMagic 24 Hour Fitness Atlassian Deutsche Telekom U.S. Department of Veterans Affairs Zalando Amdocs DB Cargo Helsana

User comments

Share your experience with using Camunda 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 Camunda and NumPy

Camunda Reviews

BPM Tools Comparison: Camunda for IT Pros vs Pneumatic for Business Users
Camunda doesn’t offer default deployments, you can’t just sign up for an account and start using it, you have to think first about how and where you plan to deploy your instance. It can be deployed on-premises or in the cloud and supports containerization technologies like Docker and Kubernetes, offering flexibility depending on the company’s IT strategy. Camunda also offers...
7 Best Business Process Management Tools (2023)
Camunda provides one of the best developer communities to help your team design, build, and automate any complicated business process, with over 100.000 developers. Having such a large network is critical for your team to have a technical reference whenever needed.
11 Business Process Management (BPM) Software for SMBs
With Camunda, you can connect, collaborate, and scale rapidly. Orchestrate Camunda into the process endpoints your organization needs to automate the flow and bring IT and business together to collaborate effectively.
Source: geekflare.com
12 of the Top-Rated Free and Open-Source BPM Software Solutions
Description: Camunda is an open-source software company providing process automation with a developer-friendly approach that is standards-based, highly scalable, and collaborative for business and IT. The vendor offers visibility into business operations and improves system resilience. The provider’s workflow and decision automation tools enable Camunda to build software...
Top 7 Workflow Software (2020 Reviews)
Camunda also offers Camunda Cloud — a workflow system that’s best for cloud computing workflows, like a shared service process.
Source: clickup.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.
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 should be more popular than Camunda. 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.

Camunda mentions (17)

  • Automating Enhanced Due Diligence in Regulated Applications
    To put everything together, you need platforms like Drools and Camunda to store the complex rule sets and logic that determine the success or failure of a due diligence attempt. - Source: dev.to / 3 months ago
  • Workflow, from stateless to stateful
    In addition, I developed a Spring Boot application with Kotlin based on the Camunda platform. Camunda is a workflow engine. - Source: dev.to / 12 months ago
  • Optimizing Decision Making with a Trie Tree-Based Rules Engine: An Experience Report
    In Pictet Technologies, my team relies a lot on decision models. These models allow our business analysts to input Compliance business rules directly into the systems with minimal developer intervention. When I joined the company, we used to use both Drools and Camunda. However, we faced severe memory and performance issues, specifically with Camunda, prompting me to explore alternatives. - Source: dev.to / over 2 years ago
  • How to Communicate Your Process Visually using BPMN as Code
    BPMN is actually a set of standards has been used for years for complex enterprise processes, and nowadays it's becoming more accessible thanks to the development of the new techniques. Web based tooling (like Camunda, BPMN.io), more platforms supporting integrating diagrams into the flows, and remote work culture all helps us to use BPMN easier. Besides all of that, we drive/lead more and more initiatives... - Source: dev.to / over 2 years ago
  • How to Achieve Geo-redundancy with Zeebe
    Bernd Ruecker is co-founder and chief technologist of Camunda as well as the author ofPractical Process Automation with O’Reilly. He likes speaking about himself in the third person. He is passionate about developer-friendly process automation technology. Connect viaLinkedIn or follow him onTwitter. As always, he loves getting your feedback. Comment below orsend him an email. - Source: dev.to / 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 Camunda and NumPy, you can also consider the following products

Appian - See how Appian, leading provider of modern low-code and BPM software solutions, has helped transform the businesses of over 3.5 million users worldwide.

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

Bizagi - Bizagi is a Business Process Management (BPMS) solution for faster and flexible process automation. It's powerful yet intuitive BPM Suite is designed to make your business more agile.

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

Kintone - Build business apps and supercharge your company's productivity with kintone's all-in-one...

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