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Fastpath Assure VS NumPy

Compare Fastpath Assure VS NumPy and see what are their differences

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Fastpath Assure logo Fastpath Assure

Fastpath Assure is a cloud GRC platform that integrates with various ERP systems

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Fastpath Assure Landing page
    Landing page //
    2023-05-11
  • NumPy Landing page
    Landing page //
    2023-05-13

Fastpath Assure features and specs

  • Comprehensive Compliance Management
    Fastpath Assure provides in-depth compliance management tools, helping organizations streamline their compliance processes with ease.
  • Real-time Monitoring and Alerts
    The solution offers real-time monitoring and alerts for unauthorized access and potential security risks, enhancing the security posture of the organization.
  • User-friendly Interface
    Fastpath Assure features an intuitive and user-friendly interface, making it accessible for users with varying levels of technical expertise.
  • Seamless Integration
    The platform easily integrates with various ERP systems such as Microsoft Dynamics, NetSuite, SAP, and Oracle, ensuring a cohesive user experience.
  • Automated Reporting
    It provides automated reporting capabilities, which save time and reduce errors in audit preparation and compliance documentation.

Possible disadvantages of Fastpath Assure

  • Cost
    Fastpath Assure can be expensive for small to medium-sized businesses, making it a potentially high-cost investment for some organizations.
  • Learning Curve
    Despite its user-friendly interface, there may still be a learning curve for new users to understand and utilize all the features effectively.
  • Customization Limitations
    The platform may have some limitations in terms of customization options, which can be a drawback for organizations with unique requirements.
  • Dependency on Internet
    As a cloud-based solution, it requires a stable internet connection for optimal performance, which can be restrictive in areas with poor connectivity.
  • Support Response Times
    Some users have reported slower response times from customer support, which can be a concern when immediate assistance is needed.

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.

Analysis of Fastpath Assure

Overall verdict

  • Yes, Fastpath Assure is considered a reliable and effective solution for companies that need robust access management controls and compliance assurance. It receives positive feedback for its ease of use, comprehensive feature set, and ability to integrate with diverse systems.

Why this product is good

  • Fastpath Assure is a recognized solution for organizations looking to manage and streamline their access governance and compliance processes. It integrates with several enterprise systems, providing real-time visibility into user access and activity. Its features, such as automated reports, risk analysis, and segregation of duties (SoD) controls, help ensure compliance with industry regulations and reduce the potential for insider threats.

Recommended for

    Fastpath Assure is recommended for medium to large enterprises across various industries, especially those that must adhere to stringent compliance standards like Sarbanes-Oxley (SOX) or GDPR. It's particularly valuable for companies using ERP systems, such as SAP or Oracle, where the risk of unauthorized access can have significant financial and operational implications.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Fastpath Assure videos

+ Add

Fastpath Assure | Security, Audit and Compliance Platform Quick View

More videos:

  • Demo - Fastpath Assure Overview Demo
  • Demo - Fastpath Assure for NetSuite - Demo | Fastpath

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 Fastpath Assure and NumPy)

Fastpath Assure

NumPy

Governance, Risk And Compliance
100 100%
Governance, Risk And Compliance
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Security & Privacy
100 100%
Security & Privacy
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 Fastpath Assure and NumPy

Fastpath Assure Reviews

We have no reviews of Fastpath Assure 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.
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 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.

Fastpath Assure mentions (0)

We have not tracked any mentions of Fastpath Assure yet. Tracking of Fastpath Assure 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 / 5 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 / 9 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 / 9 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 / 10 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 / 10 months ago
View more

What are some alternatives?

When comparing Fastpath Assure and NumPy, you can also consider the following products

SAI360 - GRC Platforms

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

ERP Maestro - The best cloud SAP GRC and SoD solutions , giving companies automated access control tools for audit, compliance, risk management and security.

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

AuditBoard - AuditBoard is a platform that offers compliance and audit management that allows auditors to analyze, manage, and report the business operations.

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

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