Need advice about which tool to choose?Ask the StackShare community!

NumPy

3K
791
+ 1
14
StreamSets

52
132
+ 1
0
Add tool

NumPy vs StreamSets: What are the differences?

What is NumPy? Fundamental package for scientific computing with Python. Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

What is StreamSets? Where DevOps Meets Data Integration. The industry's first data operations platform for full life-cycle management of data in motion.

NumPy and StreamSets belong to "Data Science Tools" category of the tech stack.

Some of the features offered by NumPy are:

  • a powerful N-dimensional array object
  • sophisticated (broadcasting) functions
  • tools for integrating C/C++ and Fortran code

On the other hand, StreamSets provides the following key features:

  • Build Batch & Streaming Pipelines in Hours
  • Map and Monitor Runtime Performance
  • Protect Sensitive Data as it Arrives

NumPy is an open source tool with 11.4K GitHub stars and 3.76K GitHub forks. Here's a link to NumPy's open source repository on GitHub.

Manage your open source components, licenses, and vulnerabilities
Learn More
Pros of NumPy
Pros of StreamSets
  • 10
    Great for data analysis
  • 4
    Faster than list
    Be the first to leave a pro

    Sign up to add or upvote prosMake informed product decisions

    Cons of NumPy
    Cons of StreamSets
      Be the first to leave a con
      • 2
        No user community
      • 1
        Crashes

      Sign up to add or upvote consMake informed product decisions

      - No public GitHub repository available -

      What is NumPy?

      Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

      What is StreamSets?

      An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.

      Need advice about which tool to choose?Ask the StackShare community!

      Jobs that mention NumPy and StreamSets as a desired skillset
      What companies use NumPy?
      What companies use StreamSets?
      Manage your open source components, licenses, and vulnerabilities
      Learn More

      Sign up to get full access to all the companiesMake informed product decisions

      What tools integrate with NumPy?
      What tools integrate with StreamSets?

      Sign up to get full access to all the tool integrationsMake informed product decisions

      Blog Posts

      GitHubPythonReact+42
      49
      40988
      What are some alternatives to NumPy and StreamSets?
      Pandas
      Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
      MATLAB
      Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
      R Language
      R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
      SciPy
      Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
      Panda
      Panda is a cloud-based platform that provides video and audio encoding infrastructure. It features lightning fast encoding, and broad support for a huge number of video and audio codecs. You can upload to Panda either from your own web application using our REST API, or by utilizing our easy to use web interface.<br>
      See all alternatives