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

Includes notes on using Apache Spark, with drill down on Spark for Physics, how to run TPCDS on PySpark, how to create histograms with Spark. Also tools for stress testing, measuring CPUs' performance, and I/O latency heat maps. Jupyter notebooks examples for using various DB systems.

Jupyter notebooks for pyspark tutorials given at University

An extension for Jupyter Lab & Jupyter Notebook to monitor Apache Spark (pyspark) from notebooks

Docker image for Jupyter notebooks with PySpark

Notebooks/materials on Big Data with PySpark skill track from datacamp (primarily). Also, contains books/cheat-sheets.

Birgitta is a Python ETL test and schema framework, providing automated tests for pyspark notebooks/recipes.

Instructions and examples for installing CNTK on an HDInsight cluster and running CNTK-Pyspark applications from Jupyter notebooks.

An example CI/CD pipeline using GitHub Actions for doing continuous deployment of AWS Glue jobs built on PySpark and Jupyter Notebooks.

Reusable Python classes that extend open source PySpark capabilities. Examples of implementation is available under notebooks of repo https://github.com/bennyaustin/synapse-dataplatform

Understanding gender differences in professional European football through Machine Learning interpretability and match actions data. This repository contains the full data pipeline implemented for the study *Understanding gender differences in professional European football through Machine Learning interpretability and match actions data*. We evaluated European male, and female football players' main differential features in-match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female (1511) and male (2700) data points were collected from event data categorized by game period and player position. Each data point included the main tactical variables supported by research and industry to evaluate and classify football styles and performance. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline had three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. A good model predicting accuracy was consistent across the different models deployed. ## Installation Install the required python packages ``` pip install -r requirements.txt ``` To handle heterogeneity and performance efficiently, we use PySpark from [Apache Spark](https://spark.apache.org/). PySpark enables an end-user API for Spark jobs. You might want to check how to set up a local or remote Spark cluster in [their documentation](https://spark.apache.org/docs/latest/api/python/index.html). ## Repository structure This repository is organized as follows: - Preprocessed data from the two different data streams is collecting in [the data folder](data/). For the Opta files, it contains the event-based metrics computed from each match of the 2017 Women's Championship and a single file calculating the event-based metrics from the 2016 Men's Championship published [here](https://figshare.com/collections/Soccer_match_event_dataset/4415000/5). Even though we cannot publish the original data source, the two python scripts implemented to homogenize and integrate both data streams into event-based metrics are included in [the data gathering folder](data_gathering/) folder contains the graphical images and media used for the report. - The [data cleaning folder](data_cleaning/) contains descriptor scripts for both data streams and [the final integration](data_cleaning/merger.py) - [Classification](classification/) contains all the Jupyter notebooks for each model present in the experiment as well as some persistent models for testing.

This Repo contains Jupyter Notebooks to recap on RDD, DataFrame, Spark Streaming and ML operations using Pyspark

PySpark notebooks to learn Apache Spark (WIP)

A collection of data analysis projects done using PySpark via Jupyter notebooks.

Python notebooks providing a tutorial for Pyspark for CSCI 4253 / 5253

Repo containing IPython notebooks using Spark/PySpark for Machine Learning and Data Analysis

Jupyter notebooks for learning PySpark

Teoría y práctica en Notebooks sobre Spark, utilizando PySpark y Spark SQL

This repo contains my learnings and practice notebooks on Spark using PySpark (Python Language API on Spark). All the notebooks in the repo can be used as template code for most of the ML algorithms and can be built upon it for more complex problems.

AI-assisted SQL migration for Databricks: convert Snowflake, T-SQL, Oracle, Teradata, Redshift, MySQL, PostgreSQL and more into Databricks SQL or PySpark notebooks. Includes validation and reconciliation features.

Jupyter Notebooks for PySpark Advanced Workshop

PySpark Notebooks

PySpark example notebooks

Adding my python,spark, pyspark, scala notebooks logics which i solve/see on daily basis,it contains optimization techniques for big data processing and real time scenarios

Common ETL patterns and utilities for PySpark. Notebooks tested on Databricks Community edition

Pyspark Demo Notebooks

Hands-on notebooks built while preparing for the Databricks Machine Learning Associate Certification. Covers PySpark, MLflow, AutoML, Model Deployment, and MLOps.

PySpark Batch and Stream Processing with Spark Dataframe API and Spark SQL. Basics on Spark ML.

Data Engineering with Databricks Study Materials

Jupyter Notebooks for PySpark Workshop using NYC Taxi Trip data