jadianes/spark-py-notebooks

Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

/ 100

Established

This project provides step-by-step guides using Jupyter notebooks to help data scientists and big data engineers learn how to analyze large datasets and build machine learning models with Apache Spark and Python. It takes raw data, like network interaction logs, and shows you how to process, explore, and build predictive models for tasks such as anomaly detection or recommendation engines. This is for professionals who need to work with massive datasets and leverage Spark's distributed computing power.

1,663 stars. No commits in the last 6 months.

Use this if you are a data scientist or big data engineer who wants to master Apache Spark for data analysis and machine learning using Python.

Not ideal if you prefer to learn Spark using R, or if you are looking for an out-of-the-box solution rather than a learning resource.

Big Data Analysis Machine Learning Data Science Training Distributed Computing Predictive Modeling

Stale 6m No Package No Dependents

Maintenance 0 / 25

Adoption 10 / 25

Maturity 16 / 25

Community 25 / 25

How are scores calculated?

Stars

1,663

Forks

911

Language

Jupyter Notebook

License

—

Compare

spark-py-notebooks and learning-apache-spark spark-py-notebooks and Spark-with-Python

Related frameworks

lensacom/sparkit-learn

PySpark + Scikit-learn = Sparkit-learn

Angel-ML/angel

A Flexible and Powerful Parameter Server for large-scale machine learning

flink-extended/dl-on-flink

Deep Learning on Flink aims to integrate Flink and deep learning frameworks (e.g. TensorFlow,...

tirthajyoti/Spark-with-Python

Fundamentals of Spark with Python (using PySpark), code examples

kaiwaehner/kafka-streams-machine-learning-examples

This project contains examples which demonstrate how to deploy analytic models to...

Explore ML Frameworks

All categories Trending ML Framework directory Insights