Best PySpark Courses 2021
Best PySpark Books 2021
Best PySpark Tutorials 2021
Spark and Python for Big Data with PySpark
Discover the latest Big Data technology – Spark! And learn how to use it with one of the most popular programming languages, Python!
One of the most valuable technology skills is the ability to analyze huge datasets, and this course is specially designed to introduce you to one of the best technologies for this task, Apache Spark! The biggest tech companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA and many more are all using Spark framework to solve their big data problems!
Spark can run up to 100 times faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the opportunity to quickly become one of the most knowledgeable people in the job market!
This course will teach the basics with a crash course in Python, continuing to learn how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we’ve done that, we’ll see how to use the MLlib machine library with DataFrame and Spark syntax. Along the way you will have simulated exercises and counseling projects that will put you directly in a real situation where you have to use your new skills to solve a real problem!
We also cover the latest Spark technologies, such as Spark SQL, Spark Streaming, and advanced models such as Gradient Boosted Trees! After completing this course, you will feel comfortable putting Spark and PySpark on your CV! This course also offers a full 30 day money back guarantee and comes with a LinkedIn certificate of
Data Science:Hands-on Diabetes Prediction with Pyspark MLlib
Pyspark is the collaboration of Apache Spark and Python. PySpark is a tool used in Big Data Analytics.
Apache Spark is an open source, clustered compute framework, built around speed, ease of use, and streaming analysis, while Python is a general purpose, high-level programming language. It provides a wide range of libraries and is primarily used for machine learning and real-time broadcast analysis. In other words, it’s a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark to tame big data. We will be using Big Data tools in this project.
Check out the most important aspect of Spark Machine Learning (Spark MLlib):
Pyspark Fundamentals and Spark Machine Learning Implementation
Importing and Using Datasets
Process data using a machine learning model using Spark MLlib
Build and train a logistic regression model
Test and analyze the model
Big Data with Apache Spark PySpark: Hands on PySpark, Python
Apache Spark can run up to 100 times faster than the Hadoop MapReduce data processing framework, which makes Apache Spark one of the most requested skills.
This PySpark tutorial will teach
Introduction to Big Data and Apache Spark
Getting started with databricks
Detailed installation step on Ubuntu – Linux Machine
Python Refresh for beginners
Apache Spark Dataframe API
Apache Spark Structured Streaming with End-to-End Example
Fundamentals of machine learning and feature engineering with Apache Spark.
Note: This course will teach only the API based on Spark 2.0 Dataframe and not the API based on RDD. As the Dataframe based API is the future of Spark.
Best PySpark Books 2021
Learning PySpark by Tomasz Drabas and Denny Lee will show how you can harness the power of Python and use it in the Spark ecosystem. You will start by understanding the Spark 2.0 architecture and learning how to set up a Python environment for Spark. Then you will get acquainted with the modules available in PySpark, such as MLib. The book will also guide you on how to summarize data with RDD and DataFrames. In the following chapters, he will become familiar with the streaming capabilities of PySpark. Towards the end, he will learn about PySpark’s machine learning capabilities using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Lastly, he will learn how to deploy his applications to the cloud using the spark-submit command.
By the end of this book, he will have a solid understanding of the Spark Python API and how it can be used to build data-intensive applications. You’re going to learn:
Learn to solve graphing and deep learning problems using GraphFrames and TensorFrames respectively
Create and interact with Spark DataFrames using Spark SQL
Read, transform and understand data and use it to train machine learning models
Develop machine learning models with MLlib
Learn how to programmatically submit your applications using Spark-Submit
Deploy locally built applications to a cluster
PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python
PySpark Cookbook by Denny Lee and Tomasz Drabas presents quick and effective recipes for harnessing the power of Python and its use in the Spark ecosystem. You will start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You will then become familiar with the modules available in PySpark and begin using them effortlessly. In addition to this, you will learn how to extract data with RDD and DataFrames, and you will understand the streaming capabilities of PySpark. You will then move on to using ML and MLlib to troubleshoot any issues with PySpark’s machine learning capabilities, and to using GraphFrames to troubleshoot graph processing issues. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command.
By the end of this book, you will be able to use the Python API for Apache Spark to solve all the problems associated with building data-intensive applications.
What you are going to learn
Configure a local PySpark instance in a virtual environment
Install and configure Jupyter in local and multi-node environments
Create DataFrames from JSON and the dictionary using pyspark.sql
Explore the clustering and regression models available in the ML module
Use DataFrames to transform the data used for modeling
Connect to PubNub and add to feeds
Frank Kane’s Taming Big Data with Apache Spark and Python
Frank Kane’s Taming Big Data with Apache Spark and Python is your companion for hands-on learning Apache Spark. Frank will begin by teaching you how to configure Spark on a single system or in a cluster, and will soon move on to analyzing large data sets with Spark RDD and developing and running Spark tasks. Efficiently and quickly with Python. Apache Spark has become the next big thing in big data, growing rapidly from rising tech to established superstar in just a few short years. Spark enables you to quickly extract actionable insights from large amounts of data, in real time, making it an essential tool in many modern businesses.
Frank Kane has packed this Apache Spark book with over 15 fun and interactive examples relevant to the real world, and will allow you to understand the Spark ecosystem and easily implement Spark projects at the production level in real time. You will:
Find out how you can identify big data issues as Spark issues
Install and run Apache Spark on your computer or on a cluster
Analyze large data sets across multiple processors using Spark’s robust distributed data sets
Implement Machine Learning in Spark with the MLlib Library
Process continuous streams of data in real time using the Spark streaming module
Perform complex network analysis with Spark’s GraphX library
Use Amazon’s Elastic MapReduce Service to Run Your Spark Jobs on a Spark Cluster
Data Analytics with Spark Using Python
Data Analytics with Spark Using Python by Jeffrey Aven covers everything you need to know to take advantage of Spark, as well as its extensions, worksets, and a broader ecosystem. The PySpark book combines a language independent introduction to the fundamental concepts of Spark with numerous programming examples using the popular and intuitive PySpark development environment. The emphasis on Python in this guide makes it widely available to a wide audience of data professionals, analysts, and developers, even those with little experience with Hadoop or Spark.
The extensive coverage ranges from basic Spark programming to advanced programming, and from Spark SQL to machine learning. You will learn how to efficiently manage all forms of data with Spark: streaming, structured, semi-structured, and unstructured. Throughout, concise topic descriptions get you up to speed quickly, and detailed hands-on exercises prepare you to solve real problems. Coverage includes:
• Understand the evolving role of Spark in the Big Data and Hadoop ecosystems
• Create Spark clusters using different deployment modes
• Control and optimize the operation of Spark clusters and applications
• Master API Spark Core RDD programming techniques
• Extend, accelerate, and optimize Spark routines with advanced API builds, including shared variables, RDD storage, and partitions
• Efficiently integrate Spark with SQL and non-relational data stores
• Perform messaging and streaming processing with Spark Streaming and Apache Kafka
• Implement predictive modeling with SparkR and Spark MLlib
Applied Data Science Using PySpark: Learn the End-to-End Predictive Model-Building Cycle
Applied Data Science Using PySpark Learn the End-to-End Predictive Model-Building Cycle by Ramcharan Kakarla, Sundar Krishnan and Sridhar Alla conHandpicked daily use case examples guide you through the end-to-end predictive modeling cycle with the latest techniques and tips in the business.
Applied Data Science with PySpark is divided into six sections that guide you through the book. In Section 1, you start with the basics of PySpark, focusing on data manipulation. We familiarize you with the language and then use it to introduce you to the mathematical functions available on the market. In Section 2, you will dive into the art of variable selection, where we will demonstrate various selection techniques available in PySpark. In Section 3, we’ll take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also talk about the different validation metrics and how to use them to choose the best models. Sections 4 and 5 review the machine learning pipelines and various methods available to get the model up and running and serving it through Docker / an API. In the final section, you’ll cover reusable objects for easy experimentation, and learn some tips that can help you optimize your machine learning programs and pipelines. You will learn:
Build an End-to-End Predictive Model
Implement various variable selection techniques
Master multiple algorithms and implementations
Learn PySpark: Build Python-based Machine Learning and Deep Learning Model
Learn PySpark Build Python-based Machine Learning and Deep Learning Model by Pramod Singh is perfect for those who want to learn to use this language to perform exploratory data analysis and solve a variety of business challenges. You’ll start by reviewing the basics of PySpark, such as the basic architecture of Spark, and learn how to use PySpark for big data processing, such as data ingestion, cleansing, and transformation techniques. This is followed by the creation of workflows to analyze the streaming data using PySpark and a comparison of different streaming platforms.
Then you will see how to schedule different Spark tasks using Airflow with PySpark and comment on tuning machine and deep learning models to get predictions in real time. This book ends with a discussion of graphics frameworks and network analysis using graphics algorithms in PySpark. All the code presented in the book will be available as Python scripts on Github. You will learn:
Develop pipelines for streaming data processing with PySpark
Build machine learning and deep learning models using the latest offerings from PySpark
Use graphical analysis with PySpark
Create Sequence Embeds from Text Data