Overview

.NET for Apache Spark

Stacks31

Followers46

Votes0

GitHub Stars2.1K

Forks329

Streamlit

Stacks403

Followers407

Votes12

GitHub Stars42.1K

Forks3.9K

.NET for Apache Spark vs Streamlit: What are the differences?

Introduction In this article, we will compare the key differences between .NET for Apache Spark and Streamlit. Both frameworks are quite popular in the world of data science and machine learning, but they have distinct features and functionalities that set them apart.

1. Integration with Spark: One major difference between .NET for Apache Spark and Streamlit is their integration with Apache Spark. .NET for Apache Spark provides a native integration with Apache Spark, allowing users to write Spark applications in .NET languages such as C# and F#. On the other hand, Streamlit does not have a direct integration with Apache Spark and is primarily focused on building interactive web applications.

2. Language Support: Another notable difference is the language support. .NET for Apache Spark supports multiple languages including C# and F#, which are widely used in the .NET ecosystem. In contrast, Streamlit primarily supports Python, which is a popular language for data science and machine learning.

3. Development Experience: When it comes to the development experience, there are some differences between the two frameworks. .NET for Apache Spark provides a familiar development experience for .NET developers, leveraging the existing .NET ecosystem and tooling. Streamlit, on the other hand, has a simpler and more streamlined development experience, with an emphasis on ease of use and rapid prototyping.

4. Visualization Capabilities: Both .NET for Apache Spark and Streamlit have visualization capabilities, but they differ in terms of their approach. .NET for Apache Spark provides integration with popular visualization libraries such as Matplotlib and Plotly, allowing users to create interactive visualizations. Streamlit, on the other hand, has built-in support for data visualization and provides a simple API for creating interactive and dynamic visualizations.

5. Deployment Options: There are differences in the deployment options offered by .NET for Apache Spark and Streamlit. .NET for Apache Spark allows users to deploy Spark applications on various platforms, including Azure and Kubernetes. Streamlit, on the other hand, is primarily designed for deploying web applications and can be hosted on platforms like Heroku, AWS, or Google Cloud.

6. Community and Ecosystem: Finally, the community and ecosystem around .NET for Apache Spark and Streamlit differ in terms of their size and maturity. .NET for Apache Spark is relatively new and its community is still growing, although it benefits from the larger .NET community. Streamlit, on the other hand, has gained significant popularity in the Python community and has an active and vibrant ecosystem with a wide range of third-party libraries and extensions.

In Summary, .NET for Apache Spark and Streamlit differ in terms of their integration with Apache Spark, language support, development experience, visualization capabilities, deployment options, and community and ecosystem size.

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Detailed Comparison

.NET for Apache Spark	Streamlit
With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data.	It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
-	Free and open source; Build apps in a dozen lines of Python with a simple API; No callbacks; No hidden state; Works with TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib, Seaborn, Altair, Plotly, Bokeh, Vega-Lite, and more
Statistics
GitHub Stars 2.1K	GitHub Stars 42.1K
GitHub Forks 329	GitHub Forks 3.9K
Stacks 31	Stacks 403
Followers 46	Followers 407
Votes 0	Votes 12
Pros & Cons
No community feedback yet	Pros 11 Fast development 1 Fast development and apprenticeship
Integrations
Apache Spark .NET F# C# Ubuntu	Python Plotly.js PyTorch Pandas Bokeh Keras NumPy Matplotlib TensorFlow Altair GraphQL

What are some alternatives to .NET for Apache Spark, Streamlit?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.