Best Spark courses & Best Spark books 2024

Best Apache Spark Courses 2022

 

Best Apache Spark Books 2022

 

Best Apache Spark Tutorials 2022

Apache Spark with Scala – Hands On with Big Data!

Completely updated and re-registered for Spark 3, IntelliJ, structured streaming, and a stronger focus on the DataSet API. Analyzing “big data” is an interesting and very valuable skill – and this course will teach you about the most popular big data technology: Apache Spark. Employers like Amazon, EBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from big data on a fault-tolerant Hadoop cluster. You will learn these same techniques, using your own Windows system right at home. It’s easier than you might think, and you’ll learn from a former engineer and senior manager at Amazon and IMDb. Spark works best when using the Scala programming language, and this course includes a crash course in Scala to get you up to speed quickly. For those who are more familiar with Python, a Python version of this class is also available: “Taming Big Data with Apache Spark and Python – Hands On”. Learn and master the art of framing data analysis problems as Spark problems through over 20 practical examples, then scale them to run on cloud services in this course including:

Learn the concepts of Spark’s resilient Datasets, DataFrames, and Distributed Datasets.
Take a crash course in the Scala programming language
Quickly develop and run Spark jobs using Scala, IntelliJ, and SBT
Translate complex analysis problems into iterative or multi-step Spark scripts
Upgrade to Larger Data Sets Using Amazon’s Elastic MapReduce Service
Understand how Hadoop YARN distributes Spark across IT clusters
Practice using other Spark technologies, such as Spark SQL, DataFrames, DataSets, Spark Streaming, Machine Learning, and GraphX
By the end of this course, you’ll be running code that analyzes gigabytes of information – in the cloud – in minutes.

This course is very practical; you will spend most of your time following up with the instructor as we write, analyze, and run real code together – both on your own system and in the cloud using Amazon’s Elastic MapReduce service. The course ends with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and Spark GraphX.

You will learn:

Frame big data analytics issues as Apache Spark scripts
Develop distributed code using the Scala programming language
Optimizing Spark Jobs Through Partitioning, Caching, and Other Techniques
Create, deploy and run Spark scripts on Hadoop clusters
Process continuous streams of data with Spark Streaming
Transform structured data using SparkSQL, DataSets and DataFrames
Traverse and analyze graph structures using GraphX
Analyze a massive dataset with Machine Learning on Spark

This is the best Apache Spark course in 2022.

Taming Big Data with Apache Spark and Python – Hands On!

Update for Spark 3, more hands-on exercises and a stronger focus on DataFrames and structured streaming. Analyzing “big data” is a hot and very valuable skill – and this course will teach you about the hottest big data technology: Apache Spark. Employers like Amazon, EBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from big data on a fault-tolerant Hadoop cluster. You will learn these same techniques, using your own Windows system right at home cluster computing. It’s easier than you think. Learn and master the art of framing data analysis problems as Spark problems through over 20 practical examples, then scale them to run on cloud services in this course including:

Learn the concepts of Spark’s DataFrames and Resilient Distributed Datastores
Quickly develop and run Spark jobs with Python
Translate complex analysis problems into iterative or multi-step Spark scripts
Upgrade to Larger Data Sets Using Amazon’s Elastic MapReduce Service
Understand how Hadoop YARN distributes Spark across IT clusters
Discover other Spark technologies, such as Spark SQL, Spark Streaming and GraphX

This course is very practical; you will spend most of your time following up with the instructor as we write, analyze, and run real code together – both on your own system and in the cloud using Amazon’s Elastic MapReduce service. 7 hours of video content is included, with over 20 real life examples of increasing complexity that you can create, run, and study on your own. Explore them at your own pace, on your own schedule. The course ends with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and GraphX.

You will learn:

Use DataFrames and structured streaming in Spark 3
Frame big data analytics issues as Spark issues
Use Amazon’s Elastic MapReduce service to run your task on a cluster with Hadoop YARN
Install and run Apache Spark on a desktop or cluster
Use Spark’s resilient distributed data sets to process and analyze large data sets across multiple processors
Implement iterative algorithms such as width-first search using Spark
Use the MLLib machine learning library to answer common data mining questions
Understand how Spark SQL enables you to work with structured data
Understand how Spark Streaming enables your processing of continuous data streams in real time
Tune in and troubleshoot big jobs running on a cluster
Share information between nodes in a Spark cluster using broadcast variables and accumulators
Understand how the GraphX ​​library helps solve network analysis problems

This is the best Apache Spark tutorial in 2022.

Scala and Spark for Big Data and Machine Learning

Learn how to use some of the most valuable tech skills on the market today, Scala and Spark! In this course, we’ll show you how to use Scala and Spark to analyze big data. Scala and Spark are two of the most in-demand skills right now, and with this course you can learn them quickly and easily! This course comes with content:

Crash Course in Scala Programming
Presentation of the Spark and Big Data ecosystem
Using Spark MLlib for Machine Learning
Scale Spark Jobs Using Amazon Web Services
Learn how to use Databrick’s Big Data platform

You will learn:

Use Scala for programming
Use Spark 2.0 DataFrames to read and manipulate data
Use Spark to process large data sets
Understand how to use Spark on AWS and DataBricks

This is among the best Apache Spark courses in 2022.

Best Apache Spark books 2022

Advanced Analytics with Spark: Patterns for Learning from Data at Scale 2nd Edition

Advanced Analytics with Spark by Sandy Ryza, Uri Laserson, Sean Owen and Josh Wills bring together Spark, statistical methods, and real-world data sets to teach you how to approach analysis problems by example. Updated for Spark 2.1, this release serves as an introduction to these techniques and other Spark programming best practices. You’ll start with an introduction to Spark and its ecosystem, then dive into models that apply common techniques, including classification, grouping, collaborative filtering, and anomaly detection, to areas like genomics, security, and finance.

If you have a basic understanding of machine learning and statistics, and are programming in Java, Python, or Scala, the templates in the book will be useful for working on your own data applications. With this book you can:

Get familiar with the Spark programming model
Get comfortable within the Spark ecosystem
Learn general approaches to data science
Examine complete implementations that analyze large public data sets
Find out which machine learning tools are right for particular problems
Acquire code adaptable to many uses

This is the best Apache Spark book in 2022.

Learning Spark: Lightning-Fast Data Analytics 2nd Edition

Learning Spark Lightning Fast Data Analytics by Jules S. Damji, Brooke Wenig, Tathagata Das and Denny Lee is updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Learning Spark are important. Specifically, Learning Spark explains how to perform simple and complex data analysis and how to use machine learning algorithms. With tutorials, code snippets, and notebooks, you can:

Learn high-level structured APIs Python, SQL, Scala, or Java
Understand Spark and SQL Engine operations
Inspect, Tune, and Debug Spark Operations with Spark Configurations and the Spark UI
Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3 or Kafka
Perform streaming and batch lightning fast big data analysis using structured streaming
Build Trusted Data Pipelines with Open Source Delta Lake and Spark
Build Machine Learning Pipelines with MLlib and Produce Models Using MLflow

This is the best Spark book in 2022.

High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark

High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark by Holden Karau and Rachel Warren demonstrates performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and development hours. Not only will you get a fuller understanding of Spark, but you will also learn how to blackmail it.

With this book you will explore:

How the new Spark SQL interfaces improve performance over the SQL RDD data structure
Choosing Between Data Joins in Core Spark and Spark SQL
Techniques for Getting the Most Out of Standard RDD Transformations
How to troubleshoot performance issues in the Spark key / value pair paradigm
Write high-performance Spark code without Scala or JVM
How to test functionality and performance when applying suggested enhancements
Using the Spark MLlib and Spark ML machine learning libraries
Spark Streaming Components and External Community Packages

Apache Spark in 24 Hours, Sams Teach Yourself

Apache Spark in 24 Hours, Sams Teach Yourself by Jeffrey Aven helps you create practical big data solutions that take advantage of Spark’s incredible speed, scalability, simplicity, and versatility.The simple, step-by-step approach in this book shows you how to implement, program, optimize, manage, integrate, and extend Spark, now and for years to come. You will learn how to create powerful solutions spanning cloud computing, real-time stream processing, machine learning, and more. Each lesson builds on what you have already learned, giving you a solid foundation for success in the real world. Whether you are a data analyst, data engineer, data scientist, or data administrator, learning Spark will help you advance your career or embark on a new career in the burgeoning field of Big Data. Learn to:

• Find out what Apache Spark does and how it fits into the big data landscape
• Deploy and run Spark locally or in the cloud
• Interact with Spark from the shell
• Take full advantage of the Spark Cluster architecture
• Develop Spark applications with Scala and functional Python
• Program with the Spark API, including transformations and actions
• Apply practical engineering / data analysis approaches designed for Spark
• Use resilient distributed data sets (RDD) for caching, persistence, and output
• Optimize the performance of the Spark solution
• Use Spark with SQL (through Spark SQL) and with NoSQL (through Cassandra)
• Take advantage of cutting-edge functional programming techniques
• Extend Spark with Streaming, R and Sparkling Water
• Get started building Spark-based machine learning and graphics processing applications
• Explore advanced messaging technologies, including Kafka
• Preview and get ready for the next generation of Spark innovations

Spark in Action: Covers Apache Spark 3 with Examples in Java, Python, and Scala 2nd Edition

Spark in Action Second Edition by Jean-Georges Perrin teaches you how to build end-to-end analytics applications. In this new book, he will learn interesting Java-based examples, including a complete data line for processing NASA satellite data. And you’ll find sample Java, Python, and Scala code hosted on GitHub that you can explore and adapt, as well as stubs that give you a cheat sheet for installing tools and understanding Spark-specific terms. You will:

Write Spark applications in Java
Spark application architecture
Ingestion through files, databases, streaming, and Elasticsearch
Query distributed data sets with Spark SQL

You will learn how to take advantage of the basic functionality and incredible processing speed of Spark, with applications that include real-time computing, lazy evaluation, and machine learning. Spark skills are a flagship product in companies around the world, and with Spark’s powerful and flexible Java APIs, you can enjoy all the benefits without having to learn Scala or Hadoop first.

This is the best Apache Spark books in 2022.

Spark: The Definitive Guide: Big Data Processing Made Simple

Use, deploy, and maintain the Apache Spark with this best Spark book written by the creators of Open Source Clustering Infrastructure. Focusing on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia split Spark’s topics into separate sections, each with a unique purpose. You’ll discover a new high-level API for creating structured streaming, end-to-end streaming applications, as well as the basic functions and general functions of Spark’s structured APIs. Developers and system administrators the basics of monitoring, tuning, and debugging Spark, and will explore Spark’s scalable machine learning library, machine learning techniques, and situations for using MLIB.

Get a smooth overview of big data and spark
DataFrames, SQL, and Datasets with concrete examples
Dive into Spark’s low-level API, RDD, and powered SQL and dataframe
Understand how the spark moves in the clutter
Debug, monitor and tune spark clusters and applications
Discover Flow Processing Engine, the power of structured streaming
Apply MLIB on a variety of issues, including classification or recommendations

Learn Apache Spark from the best Apache Spark book in 2022.

Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming

You must first understand how to handle data in real time before you can construct analytics tools to acquire quick insights. Developers familiar with Apache Spark will learn how to use this in-memory framework for streaming data with this practical guide. You’ll see how Spark makes it possible to write streaming jobs in a similar way to batch jobs.

Authors Gerard Maas and François Garillot guide you through Apache Spark’s theoretical foundations. Two sections of this detailed book analyse and contrast the two streaming APIs that Spark now supports: the old Spark Streaming library and the updated Structured Streaming API. You will:

Learn the basics of stream processing and look at several streaming topologies.
Investigate Structured Streaming using real-world examples, and learn about various aspects of stream processing in depth.
Spark Streaming allows you to create and run streaming jobs and applications, as well as link Spark Streaming with other Spark APIs.
Approximation algorithms and machine learning algorithms are among the sophisticated Spark Streaming techniques covered.
Stream processing projects such as Apache Storm, Apache Flink, and Apache Kafka Streams are compared to Apache Spark.

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