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  5. .NET for Apache Spark vs Hummingbird

.NET for Apache Spark vs Hummingbird

OverviewComparisonAlternatives

Overview

.NET for Apache Spark
.NET for Apache Spark
Stacks31
Followers46
Votes0
GitHub Stars2.1K
Forks329
Hummingbird
Hummingbird
Stacks4
Followers8
Votes0
GitHub Stars3.5K
Forks286

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

# Introduction
In the realm of big data processing, .NET for Apache Spark and Hummingbird are two popular frameworks, each with its unique characteristics and capabilities.

1. **Programming Language Compatibility**: .NET for Apache Spark is deeply integrated with the .NET ecosystem, enabling developers to use C#, F#, and other .NET languages, whereas Hummingbird primarily focuses on bridging the gap between Apache Spark and the Python ecosystem.
2. **Performance Optimization**: .NET for Apache Spark leverages the performance benefits of the .NET runtime, providing optimized execution for .NET-based applications, while Hummingbird focuses on enhancing Python-based applications' performance by restructuring the execution flow.
3. **Data Processing Flexibility**: .NET for Apache Spark offers a broad range of data processing capabilities, including streaming, machine learning, and SQL queries, while Hummingbird specializes in optimizing Spark's dataframe operations specifically for Python-centric workloads.
4. **Integration with Existing Infrastructure**: .NET for Apache Spark seamlessly integrates with existing .NET applications and libraries, allowing for simplified deployment and cross-platform compatibility, whereas Hummingbird is designed to enhance Python's interoperability with Apache Spark without major architectural changes.
5. **Community Support and Ecosystem**: .NET for Apache Spark benefits from the large and active .NET community, providing a wealth of resources, documentation, and libraries, while Hummingbird, being a more specialized tool, may have a smaller but dedicated community focused on Python and Spark integration.
6. **Type System and Compiler Optimization**: .NET for Apache Spark takes advantage of .NET's strong static typing system and compiler optimizations for improved performance and type safety, whereas Hummingbird augments Python's dynamic typing with optimizations tailored to Spark's dataframe operations.

In Summary, .NET for Apache Spark and Hummingbird offer distinct advantages in terms of language compatibility, performance optimization, data processing flexibility, infrastructure integration, community support, and type system optimization.

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Detailed Comparison

.NET for Apache Spark
.NET for Apache Spark
Hummingbird
Hummingbird

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 a library for compiling trained traditional ML models into tensor computations. It allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models.

-
Current and future optimizations implemented in neural network frameworks; Native hardware acceleration; Convert your trained traditional ML models into PyTorch
Statistics
GitHub Stars
2.1K
GitHub Stars
3.5K
GitHub Forks
329
GitHub Forks
286
Stacks
31
Stacks
4
Followers
46
Followers
8
Votes
0
Votes
0
Integrations
Apache Spark
Apache Spark
.NET
.NET
F#
F#
C#
C#
Ubuntu
Ubuntu
Linux
Linux
XGBoost
XGBoost
PyTorch
PyTorch
macOS
macOS
Windows
Windows
scikit-learn
scikit-learn

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

TensorFlow

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

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

PyTorch

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

Keras

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

Kubeflow

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

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

Polyaxon

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

Streamlit

Streamlit

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.

MLflow

MLflow

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

H2O

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.

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