Learn Apache Spark 2020 – Best Apache Spark courses & Best Apache Spark tutorials

Best Apache Courses 2020

 

Best Apache Tutorials 2020

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.

We’re going to have fun along the way. You’ll warm up with some easy examples of using Spark to analyze movie rating data and book text. Once you have the basics under your belt, we’ll move on to more complex and interesting tasks. We’ll use a million movie ratings to find movies that look alike, and you might even discover new movies that you might like in the process! We’ll analyze a social graph of superheroes, and find out who is the most “popular” superhero – and develop a system to find “degrees of separation” between superheroes. Are all Marvel superheroes within a few degrees of being connected to SpiderMan? You will find the answer.

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. over 8 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:

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 2020.

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. 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

By the end of this course, you’ll be running code that analyzes gigabytes of information – in the cloud – in minutes. This course uses the familiar Python programming language; if you prefer to use Scala to get the best performance from Spark, check out my course “Apache Spark with Scala – Hands On with Big Data” instead. We’re going to have fun along the way. You’ll warm up with some easy examples of using Spark to analyze movie rating data and book text. Once you have the basics under your belt, we’ll move on to more complex and interesting tasks. We’ll use a million movie ratings to find movies that look alike, and you might even discover new movies that you might like in the process! We’ll analyze a social graph of superheroes, and find out who is the most “popular” superhero – and develop a system to find “degrees of separation” between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You will find the answer.

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 2020.

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’s MLlib for Machine Learning
Scale Spark Jobs Using Amazon Web Services
Learn how to use Databrick’s Big Data platform

This course comes with complete projects for you, including topics like Analyzing Financial Data or Using Machine Learning to Rank Ecommerce Customer Behavior! We teach the latest Spark 2.0 methodologies so you can learn how to use SparkSQL, Spark DataFrames, and Spark MLlib! After completing this course you will feel comfortable putting Scala and Spark on your CV!

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 2020.

Best Apache Spark books 2020

Spark: The Definitive Guide: Big Data Processing Made Simple

Spark: The Definitive Guide: Big Data Processing Made Simple
  • Amazon Kindle Edition
  • Chambers, Bill (Author)
  • English (Publication Language)
  • 936 Pages - 02/08/2018 (Publication Date) - O'Reilly Media (Publisher)

Use, deploy, and maintain the Apache Spark with this best Spark guide written by the creators of Open Source Clustering Infrastructure. Focusing on improvements and new features in Spark 2.0, authors Bill Chambers and Metei Jaharia 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 2020.

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