Learning apache spark with python github Comput. Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. Loan Default Prediction using PySpark, with jobs scheduled by Apache Airflow and Integration with Spark using Apache Livy Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. Why Spark? ¶ I think the following four main reasons from Apache Spark™ official website are good enough to convince you to use Spark. It contains the example code and solutions to the exercises in O'Reilly upcoming book Machine Learning with Apache Spark by Adi Polak. The first version was posted on Github in ChenFeng ([Feng2017]). Prelim Notes for Numerical Analysis, The University of Tennessee, Knoxville Quick Start Interactive Analysis with the Spark Shell Basics More on Dataset Operations Caching Self-Contained Applications Where to Go from Here This tutorial provides a quick introduction to using Spark. That means you can freely copy and adapt these code snippets and you Nov 8, 2024 · Apache Spark comes with MLlib, a machine learning library built on top of Spark that you can use from a Spark pool in Azure Synapse Analytics. This function will save a lot of time for you. yaozeliang / Learning-Apache-Spark-with-Python Public Notifications You must be signed in to change notification settings Fork 0 Star 0 yaozeliang / Learning-Apache-Spark-with-Python Public Notifications You must be signed in to change notification settings Fork 0 Star 0 This is the repository for the LinkedIn Learning course Apache PySpark by Example. About the Book Frank Kane’s Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. JDBC Connection 26. Contribute to databricks/learning-spark development by creating an account on GitHub. It contains all the supporting project files necessary to work through the book from start to finish. May 19, 2025 · PySpark combines Python’s learnability and ease of use with the power of Apache Spark to enable processing and analysis of data at any size for everyone familiar with Python. Azure Machine Learning offers a fully managed, serverless, on-demand Apache Spark compute cluster. 4. The PySpark tutorial by Wenqiang Feng with PDF - Learning Apache Spark with Python. Support includes PySpark, which allows users to interact with Spark using familiar Spark or Python interfaces. Welcome to my Learning Apache Spark with Python note! In this note, you will learn a wide array of concepts about PySpark in Data Mining, Text Mining, Machine Learning and Deep Learning. Contribute to plthiyagu/CheatSheet development by creating an account on GitHub. You’ll explore all core concepts and tools within the Spark ecosystem, such as Spark Streaming, the Spark Streaming API, machine learning extension, and structured streaming. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. Using PEX Spark SQL Apache Arrow in PySpark Python User-defined Table Functions (UDTFs) Python Data Source API Python to Spark Type Conversions Pandas API on Spark Options and settings From/to pandas and PySpark DataFrames Transform and apply a function Type Support in Pandas API on Spark Type Hints in Pandas API on Spark From/to other DBMSes This repository is part of a series on Apache Spark examples, aimed at demonstrating the implementation of Machine Learning solutions in different programming languages supported by Spark. One key thing different between pandas and spark is you need to ask spark to persist your dataframe. The feedforward neural network was the first and simplest type of artificial neural network devised. Explanation of all PySpark RDD, DataFrame and SQL examples present on this project are available at Apache PySpark Tutorial, All these examples are coded in Python language and tested in our development environment. Learning Apache Spark, Github 2017. The repository also contains a number of small example notebooks. With the help of the user defined function, you can get even more statistical results. In this book, we will guide you through the latest incarnation of Apache Spark using Python. Contribute to AzureMentor/mmlspark development by creating an account on GitHub. Contribute to piotrszul/spark-tutorial development by creating an account on GitHub. Gradient Descent in 1D and Gradient Descent in 2D for 1D and 2D, respectively) and with learning rate (search step) . Its With the Apache Spark framework, Azure Machine Learning serverless Spark compute is the easiest way to accomplish distributed computing tasks in the Azure Machine Learning environment. You can define resources 12. Its not a big problem to follow this book considering the fact that the python api is extremely similar to the Java API. NET for Apache Spark. NET for Apache Spark code focused on simple and minimalistic scenarios. Contribute to adrianquiroga/Machine-Learning-with-Apache-Spark development by creating an account on GitHub. Markov Chain Monte Carlo 19. To follow along with this guide PySpark is the Python API for Apache Spark, an open source, distributed computing framework and set of libraries for real-time, large-scale data processing. Introduction ¶ A feedforward neural network is an artificial neural network wherein connections between the units do not form a cycle. Apache Spark - A unified analytics engine for large-scale data processing - apache/spark 2. 5. This shared repository mainly contains the self-learning and self-teaching notes from Wenqiang during his IMA Data Science Fellowship. Contribute to MingChen0919/learning-apache-spark development by creating an account on GitHub. It enables you to perform real-time, large-scale data processing in a distributed environment using Python. Contribute to databricks/spark-deep-learning development by creating an account on GitHub. Apache Spark is an open source distributed general-purpose cluster-computing framework. Orielly learning spark : Chapter’s 3,4 and 6 for 50% ; Chapters 8,9 (IMP) and 10 for 30% Programming Languages (Certifications will be offered in Scala or Python) Some experience developing Spark apps in production already Developers must be able to recognize the code that is more parallel, and less memory Through this repository, readers are encouraged to engage in collaborative learning, fostering a dynamic community dedicated to mutual growth and development. Java is the only language not covered, due to its many disadvantages (and not a single advantage) compared to Project uses Apache Spark functionalities (SparkSQL, Spark Streaming, MLib) to build machine learning models (Batch Processing-Slow) and then apply the model with (Spark Streaming-Fast) to predict new output. You'll learn all about the core concepts and tools within the Spark Mar 7, 2010 · Machine Learning for Big Data using PySpark with real-world projects About this Repo This repository provides a set of self-study tutorials on Machine Learning for big data using Apache Spark (PySpark) from basics (Dataframes and SQL) to advanced (Machine Learning Library (MLlib)) topics with practical real-world projects and datasets. You will start by getting a firm understanding of the Spark 2. PySpark Tutorial for Beginners - Practical Examples in Jupyter Notebook with Spark version 3. What sites/courses/etc have you found helpful? Ideally free, open source resources. Automation for Cloudera Distribution Hadoop 21. The PDF version can be downloaded from HERE. You can analyze data using Python through Spark batch job definitions or with interactive Fabric notebooks. 6. Microsoft Machine Learning for Apache Spark. The contents of the VM are: Apache Spark 3. Apache Spark for data engineers. HuggingFace Model: Performs sentiment analysis on incoming reviews. Example code from Learning Spark book. Among other things, it can: It focuses on problems that have a small amount of data and that can be run in parallel. This shared repository mainly contains notes and projects which from Ming's Big data class and Wenqiang's IMA Data Fellows' projects. The full course is available from LinkedIn Learning. The continuous improvements on Apache Spark lead us to this discussion on how to do Deep Learning with it. The official Apache Spark documentations. You train your skills with Spark transformations and actions and you work with Jupyter Notebooks on Docker. Chen. Java is the only language not covered, due to its many disadvantages (and not a single advantage) compared to I am creating Apache Spark 3 - Spark Programming in Python for Beginners course to help you understand the Spark programming and apply that knowledge to build data engineering solutions. Cheatsheet. Spark with Python Apache Spark Apache Spark is one of the hottest new trends in the technology domain. If you find your work wasn’t cited in this note, please feel free to let us know. Written by the developers of Spark, this book will have data scientists and PySpark Cheat Sheet - learn PySpark and develop apps faster View on GitHub PySpark Cheat Sheet This cheat sheet will help you learn PySpark and write PySpark apps faster. There are two types of samples/apps in the . - Upasna22/Twitter-Sentiment-Analysis-using-Apache . A Monte Carlo simulator helps Distributed Deep learning with Keras & Spark. The book has a github repo as well so you have access to lots of data there to work with. We try to use the detailed demo code and examples to show how to use pyspark for big data mining. For small datasets, it distributes the search for estimator The above figure source: Blast Analytics Marketing RFM is a method used for analyzing customer value. It is the framework with probably the highest potential to realize the fruit of the marriage between Big Data and Machine Learning. It provides high-level APIs in Scala, Java, Python, and R (Deprecated), and an optimized engine that supports general computation graphs for data analysis. Spark Application for analysis of Apache Access logs and detect anamolies! Along with Medium Article. narrow transformations : Transformations like Map and Filter that RDD represents Resilient Distributed Dataset. How can you work with it efficiently? This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. Speed Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. This is the shared repository for Learning Apache Spark Notes. Want to get up and running with Apache Spark as soon as possible? If you're well versed in Python, the Spark Python API (PySpark) is your ticket to accessing the Notes on Apache Spark (pyspark). Wenqiang Feng. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. Apache Spark 4. com I’ve been looking to compile some different spark/pyspark learning links into a repo for reference. Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks Some exercises to learn Spark. We will be taking a live coding approach and explain all the needed concepts along the way. MongoDB Atlas: Temporary storage for streaming data. More details can be found at Wikipedia RFM_wikipedia. ALS: Stock Portfolio Recommendations 17. Following is what you need for this book: This book is for data engineers, data scientists, and data practitioners who want to learn how to build efficient and scalable data pipelines using Apache Spark, Delta Lake, and Databricks. Contribute to maxpumperla/elephas development by creating an account on GitHub. Phys. Contribute to databricks/spark-training development by creating an account on GitHub. , 334:45–67, 2016. Or you can cd to the chapter directory and build jars as specified in each README. This package contains some tools to integrate the Spark computing framework with the popular scikit-learn machine library. This repository is part of a series on Apache Spark examples, aimed at demonstrating the implementation of Machine Learning solutions in different programming languages supported by Spark. This goes in with a bit more details than the previous book. The tutorial covers various topics like Spark Introduction, Spark Installation, Spark RDD Transformations and Actions, Spark DataFrame, Spark SQL, and more. D. Project uses Apache Spark functionalities (SparkSQL, Spark Streaming, MLib) to build machine learning models (Batch Processing-Slow) and then apply the model with (Spark Streaming-Fast) to predict new output. The Introduction to Apache Spark course by A. There are more guides shared with other languages such as Quick Start in Programming Guides at the Spark documentation. 0 is a framework that is supported in Scala, Python, R, and Java. This course is example-driven and follows a working session like approach. The reader is referred to the repository https://github. 40 questions, 90 minutes 70% programming Scala, Python and Java, 30% are theory. We will show you how to read structured and unstructured data, how to use some fundamental data types available in PySpark, how to build machine learning models, operate on graphs, read streaming data and deploy your models in the cloud. Social Network Analysis 16. Deep Learning Pipelines for Apache Spark. Contribute to Marlowess/spark-exercises development by creating an account on GitHub. Histogram for the Metropolis algorithm with python shows a trace plot for this run as well as a histogram for the Metropolis algorithm compared with a draw from the true normal density. This project implements a sophisticated movie recommender system using various collaborative filtering techniques and machine learning algorithms. All components are containerized with Docker for easy deployment and scalability. You'll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. Each RDD is split into multiple partitions (similar pattern with smaller sets), which may be computed on different nodes of the cluster. If you're already familiar with Python and libraries such as Pandas, then PySpark is a good language to learn to create more scalable analyses and pipelines. This book presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. Neural Network 20. Microsoft Fabric provides built-in Python support for Apache Spark. I am creating Apache Spark 3 - Real-time Stream Processing using Python course to help you understand the Stream Processing using Apache Spark and apply that knowledge to build stream processing solutions. Getting Started # This page summarizes the basic steps required to setup and get started with PySpark. Apache Spark has an advanced DAG execution engine that supports acyclic data flow and in-memory computing. This repository contains Apache Spark based projects in either Python or Scala. I created a detailed timeline of the development of Apache Spark until now to see how we got here. As the most active open-source project in the big data community, Apache SparkTM has become the de-facto standard for big data processing and analytics. Apache Spark is an open-source cluster-computing framework. Experimented with three classifiers -Naïve Bayes, Logistic Regression and Decision Tree Learning and performed k-fold cross validation to determine the best. Dec 21, 2020 · MLlib is Apache Spark’s machine learning library, with APIs in Java, Scala, Python, and R 1 2 3. Note: the latest information is here. py. MLlib provides many utilities useful for machine learning tasks, such as: classification, regression, clustering and dimentionality reduction. To access the current body of courseware, please sign in to Databricks Academy using one of the following three options: Spark-Streaming-In-Python Public Apache Spark 3 - Structured Streaming Course Material Python 125 164 Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks - jadianes/spark-py-notebooks This repository contains hands-on examples, mini-projects, and exercises for learning and applying Apache Spark using PySpark (Python API). In this repository, we try to use the detailed demo code and examples to show how to use each main DataCamp Python Course . Using PEX Spark SQL Apache Arrow in PySpark Python User-defined Table Functions (UDTFs) Python Data Source API Python to Spark Type Conversions Pandas API on Spark Options and settings From/to pandas and PySpark DataFrames Transform and apply a function Type Support in Pandas API on Spark Type Hints in Pandas API on Spark From/to other DBMSes Spark is a fast and general cluster computing system for Big Data. These snippets are licensed under the CC0 1. To get the most out of this book, you should have basic knowledge of data architecture, SQL, and Python programming. An end-to-end data engineering pipeline that orchestrates data ingestion, processing, and storage using Apache Airflow, Python, Apache Kafka, Apache Zookeeper, Apache Spark, and Cassandra. Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks - raidery/spark-ml-labs Machine Learning projects Spark ML projects done as part of edX course Apache Spark on Azure HDInsight, using Spark ML in both Python and Scala programming languages. Jun 20, 2025 · Learn how PySpark processes big data efficiently using distributed computing to overcome memory limits and scale your Python workflows. CONTENTS. Note: last update in Dec 2022. Written by the developers of Spark, this book will have data scientists and May 23, 2025 · PySpark Overview ¶ Date: May 23, 2025 Version: 3. In our PySpark tutorial video, we covered various topics, including Spark installation, SparkContext, SparkSession, RDD transformations and actions, Spark DataFrames, Spark SQL, and more. PySpark supports all of Spark’s features such as Spark SQL, DataFrames, Structured Streaming, Machine Learning (MLlib) and Spark Core. Contribute to HaDock404/Books development by creating an account on GitHub. Chapters 2, 3, 6, and 7 contain stand-alone Spark applications. It is completely free on YouTube and is Mar 3, 2019 · These features make Python and Spark ideal tools for handling data and implementing machine learning algorithms in our experiment. Clustering 13. Batch Gradient Descent ¶ Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. Solved in Python. Learning Apache Spark with Python. About this note ¶ This is a shared repository for Learning Apache Spark Notes. 12 with a scientific Python stack (scipy, numpy, matplotplib, pandas, statmodels, scikit-learn, gensim, networkx, seaborn, pylucene and a few others) plus IPython 8 + Jupyter notebook Microsoft Machine Learning for Apache Spark. PySpark combines Python’s learnability and ease Databricks Certified Associate Developer for Apache Spark 3. Wrap PySpark Package 22. 1. This project aims to build a real-time fraud detection system using Apache Kafka for data ingestion and Apache Spark for data processing and machine learning. Below are different implementations of Spark. Awesome Spark A curated list of awesome Apache Spark packages and resources. I highly recommend you to use my get_dummy function in the other cases. J. More details can be found at A Zero Math Introduction to Markov Chain Monte Carlo Methods. The system is built using Apache Spark and PySpark, leveraging the power of distributed computing for handling large-scale movie rating data. M. Text Mining 15. A guide covering Apache Spark including the applications, libraries and tools that will make you better and more efficient with Apache Spark development. An RDD in Spark is simply an immutable distributed collection of objects sets. Each notebook contains steps for data ingestion, exploration, cleansing, transformation, training, and prediction. End-End apps/scenarios - Real world examples of industry standard benchmarks, usecases and business applications implemented using . mllib. Apache Spark: Used for real-time data processing. Monte Carlo simulations are just a way of estimating a fixed parameter by repeatedly generating random numbers. A very useful and widely used tool for doing that is Apache Spark. Monte Carlo Simulation 18. 0 Universal License. We welcome contributions to both categories! or Spark in Action, Manning publications this book is primarily written in Java, but the github repo has code for, Java, Python and Scala. With this repo, I am documenting it! ***How Apache Spark builds a DAG and Physical Execution Plan ? *** a. I am creating Apache Spark 3 - Spark Programming in Python for Beginners course to help you understand the Spark programming and apply that knowledge to build data engineering solutions. Thanks! A comprehensive, hands-on learning path for mastering Apache Spark with Python. The describe function in pandas and spark will give us most of the statistical results, such as min, median, max, quartiles and standard deviation. These Jupyter notebooks are designed to complement the video content, allowing you to follow along, experiment, and practice your PySpark skills. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. Preconditioned Steepest Descent Methods for some Nonlinear Elliptic Equations Involving p-Laplacian Terms. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. We will first introduce the API through Spark’s interactive shell (in Python or Scala), then show how to write applications in Java, Scala, and Python. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. 0 architecture and how to set up a Python environment for Spark. Spark is a unified analytics engine for large-scale data processing. Introduction This project gives you an Apache Spark cluster in standalone mode with a JupyterLab interface built on top of Docker. Feng2016PSD Feng, A. Contribute to runawayhorse001/LearningApacheSpark development by creating an account on GitHub. Also see GitHub Project Page. I'm reading this book and applying all I learnt in Python for each chapter. This repository contains 8 interactive Jupyter notebooks that take you from PySpark fundamentals to advanced topics like machine learning and recommendation systems. Demo I applied my img2txt function to the image in Image folder. 1. It runs fast (up to 100x faster than traditional Hadoop MapReduce due to in-memory operation, offers robust, distributed, fault-tolerant data objects (called RDD), and integrates beautifully Learning Spark 2nd Edition Welcome to the GitHub repo for Learning Spark 2nd Edition. You will get familiar with This project gives you an Apache Spark cluster in standalone mode with a JupyterLab interface built on top of Docker. This course shows you how you can use Spark to make your overall analysis workflow faster and more efficient. You can build all the JAR files for each chapter by running the Python script: python build_jars. RFM stands for the three dimensions: Recency – How recently did the customer purchase? i Apache Spark is a unified analytics engine for large-scale data processing. It covers key Spark concepts such as: RDD operations (transformations and actions) DataFrame creation and manipulation Working with Spark SQL Aggregations and group operations Real-world data processing - GitHub - ali2yman/Practical-PySpark: This This project aims at teaching you the Apache Spark MLlib in python. You’ll then get Introduction This repository contains mainly notes from learning Apache Spark by Ming Chen & Wenqiang Feng. About Apache Spark & Python (PySpark) tutorials and Machine Learning applications as Jupyter notebooks Apache Spark training material. MultiLayer This is the code repository for Learning Apache Spark 2, published by Packt. A comprehensive collection of my learning journey with Apache Spark, covering core concepts, hands-on examples. Wang, S. Feng and M. My Cheat Sheet 25. It searchs with the direction of the steepest desscent which is defined by the negative of the gradient (see Fig. Databricks Tips 27 May 23, 2025 · PySpark Overview ¶ Date: May 23, 2025 Version: 3. Apache Spark is one of the hottest new trends in the technology domain. It is assumed that you have some basic experience with programming in Scala, Java, or Python and have some basic knowledge of machine learning, statistics, and data analysis. RFM Analysis 14. LearningApacheSpark. You'll learn all about the core concepts and tools within the Spark Note In this demo, I introduced a new function get_dummy to deal with the categorical data. Reply reply on_the_mark 19. Though I am using Spark from quite a long time now, I never noted down my practice exercise. In this network, the information moves in only one direction, forward (see Fig. NET for Apache Spark repo: Getting Started - . I had some benefit in working at Databricks and picked up spark while in security. Everything in here is fully functional PySpark code you can run or adapt to your programs. A library of books on Data Science and IT. In essence, the shared repository for Learning Apache Spark Notes epitomizes the spirit of knowledge sharing and collaborative learning. This repository includes code snippets, tutorials, and practical implementations using Python for distributed data processing, transformations, and machine learning workflows. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph Histogram for the Metropolis algorithm with python ¶ Figure. Feng2014 Feng. As such, it is different from recurrent neural networks. 4 Python 3. Apache Kafka (Confluent Cloud): Handles data ingestion and message brokering. PySpark is the Python API for Apache Spark. A guide covering Apache Airflow including the applications, libraries and tools that will make you better and more efficient with Apache Airflow development. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX Oct 12, 2017 · Apache Spark is an open source distributed general-purpose cluster-computing framework. All code and diagrams used in the book are available here for free. December 05, 2021. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the Apache Software Foundation, which has maintained it since. Monte Carlo simulation is a technique used to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models. Learning is a continuous process. Spark – Default interface for Scala and Java PySpark – Python interface for Spark SparklyR – R interface for Spark. Learn Apache Spark through its Scala, Python (PySpark) and R (SparkR) API by running the Jupyter notebooks with examples on how to read, process and write data. Tutorial and examples for using Apache Spark. It also provides a PySpark shell for interactively analyzing your data. Contribute to tomaztk/Spark-for-data-engineers development by creating an account on GitHub. Big Data Processing with Apache Spark teaches you how to use Spark to make your overall analytical workflow faster and more efficient. Joseph, University of California, Berkeley. Spark pools in Azure Synapse Analytics also include Anaconda, a Python distribution with various packages for data science including machine learning. With the following software and hardware list 9. 12 A virtualenv for Python 3. The courseware materials for this course are no longer available through GitHub. Salgado, C. Learning Ray - Flexible Distributed Python for Machine Learning Jupyter notebooks and other resources for the upcoming book "Learning Ray" (O'Reilly). This repository demonstrates big data processing, visualization, and machine learning using tools such as Hadoop, Spark, Kafka, and Python. You don't need to create both an Azure Synapse workspace and a Synapse Spark pool. There are live notebooks where you can try PySpark out without any other step: Live Notebook: DataFrame Live Notebook: Spark Connect Live Notebook: pandas API on Spark The A guide covering Apache Beam including the applications, libraries and tools that will make you better and more efficient with Apache Beam development. Wise. Spark’s ease of use, versatility, and Performed Feature Extraction and transformation from the JSON format of tweets using machine learning package of python pyspark. It is intended that each directory contain both implementations. The notebooks can be read online, as we add more and more explanations in the online version. 6 Useful links: Live Notebook | GitHub | Issues | Examples | Community PySpark is the Python API for Apache Spark. A comprehensive explanation each project and it's specifications are within the project's directory. Contribute to ML-BigData-Tools/mmlspark development by creating an account on GitHub. RDD represents Resilient Distributed Dataset. Learning Spark Data in all domains is getting bigger. 0 - ericbellet/databricks-certification "Apache Spark the Definitive Guide" from the founders of Spark itself. Zeppelin to jupyter notebook 24. Note: archived. PySpark Data Audit Library 23. It is commonly used in database marketing and direct marketing and has received particular attention in retail and professional services industries. This article provides an overview of developing Spark applications in Synapse using the Python language. A comprehensive, hands-on learning path for mastering Apache Spark with Python. Processing big data in real-time is challenging due to scalability, information consistency, and fault tolerance. LearningJournal / Spark-Programming-In-Python Public Notifications You must be signed in to change notification settings Fork 593 Star 466 The continuous improvements on Apache Spark lead us to this discussion on how to do Deep Learning with it. Python: Used to develop the Kafka producer, Spark stream processor, and data analysis scripts. In this Apache Spark Fundamentals training, you learn about the Spark architecture and the fundamentals of how Spark works. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance Feng and M. Spark Python Notebooks This is a collection of IPython notebook / Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language.