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Sisense VS NumPy

Compare Sisense VS NumPy and see what are their differences

Draxlr
Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

Sisense logo Sisense

The BI & Dashboard Software to handle multiple, large data sets.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Sisense Landing page
    Landing page //
    2023-10-11

Behind Sisense's drag-and-drop user interface and eye-grabbing visualization options lies a technology that forever changes the world of business analytics software. By removing limitations to data size and performance imposed by in-memory and relational databases, Sisense enables any business to deliver interactive terabyte-scale analytics to thousands of users within hours

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

Sisense features and specs

  • Self-Service Analytics
    Sisense allows users to create, analyze, and visualize data through a straightforward drag-and-drop interface, which significantly reduces dependency on IT teams.
  • Scalability
    The platform is built to handle large datasets and can scale up efficiently to meet growing business needs, ensuring performance remains stable as data complexity increases.
  • Integrations
    Sisense offers robust integrations with numerous data sources, including databases, cloud services, and third-party applications, making it easy to unify data from across the organization.
  • Embedded Analytics
    The product provides strong embedded analytics capabilities, allowing businesses to integrate advanced analytics directly into their own applications and workflows.
  • Customizable Dashboards
    Users can create highly customizable dashboards tailored to specific business requirements, enabling more insightful and actionable data visualization.

Possible disadvantages of Sisense

  • Complexity for Novices
    While powerful, the platform has a steep learning curve for users who are not familiar with BI tools, requiring either training or a background in data analysis to leverage its full potential.
  • Cost
    Sisense can become expensive, particularly for small and medium-sized businesses, as pricing may increase with the addition of more users and data volume.
  • Performance Issues
    Some users report performance issues when dealing with extremely large datasets or complex queries, which can hinder real-time analytics and decision-making.
  • Customer Support
    Several users have mentioned that customer support can sometimes be slow to respond or resolve issues, which can be frustrating during critical business operations.
  • Limited Advanced Analytics
    While Sisense excels in self-service and embedded analytics, it may be less effective for advanced data science tasks such as machine learning and predictive analytics compared to specialized tools.

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.

Sisense videos

+ Add

I Evaluated 4 BI Tools: Power BI, Tableau, Google Data Studio, & Sisense. Here's What I Found.

More videos:

  • Review - Sisense Business Intelligence Software: Product Spotlight
  • Demo - Sisense Product Demo

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 Sisense and NumPy)

Sisense

NumPy

Data Dashboard
89 89%
Data Dashboard
11% 11
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Business Intelligence
100 100%
Business Intelligence
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Sisense 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 Sisense and NumPy

Sisense Reviews

Explore 7 Tableau Alternatives for Data Visualization and Analysis
Sisense is a top business intelligence tool that converts complex data into useful insights. Sisense's Elastic Data Engine (EDT) enables fast query performance and real-time analytics. It provides a simple interface for data processing, viewing, and sharing. Sisense scales quickly, offers advanced analytics, and protects data. Its mobile apps provide on-the-go access to...
Source: www.draxlr.com
10 Best Alternatives to Looker in 2024
Sisense: Sisense excels at merging complex data from multiple sources into actionable insights, making it perfect for businesses handling diverse data sets. Its drag-and-drop interface simplifies the analytics process, making it accessible even to users with limited technical expertise.
Source: www.sigmacomputing.com
6 Best Looker alternatives
Like Looker, Sisense doesn’t release its pricing – they custom build quotes based on the number of users and data size. Reviews suggest that plans typically start at $17,000 per year.
Source: trevor.io
Top 10 AI Data Analysis Tools in 2024
One of the standout features of Sisense is its ability to visualize AI and machine learning-enhanced analytics through clear charts and graphs. Additionally, it supports natural language queries, allowing users to ask questions in everyday language and receive insights generated by natural language generation and generative AI technologies.
Source: powerdrill.ai
5 best dashboard building tools for SQL data in 2024
Sisense is the last business intelligence platform on our list, and it was founded in 2004. It operates on a single-stack architecture to provide insights as dashboards.
Source: www.draxlr.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 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.

Sisense mentions (0)

We have not tracked any mentions of Sisense yet. Tracking of Sisense 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 / 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 Sisense and NumPy, you can also consider the following products

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

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

Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.

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

Microsoft Power BI - BI visualization and reporting for desktop, web or mobile

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

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