Struggling to conquer Apache Spark?

Learning is hard enough as it is but when you bring in distributed computing frameworks in sophisticated programming languages - things don't get any easier. While self-study can certainly help, without a good guide, things are always more difficult than they should be. That's why I created Spark Tutorials, to make it easier to learn and use Apache Spark. is here to provide simple, easy to follow tutorials to help you get up and running quickly. You'll learn the foundational abstractions in Apache Spark from RDDs to DataFrames and MLLib. Start off with some of the articles below.

Spark Clusters on AWS EC2 - Reading and Writing S3 Data - Predicting Flight Delays with Spark Part 1

In this tutorial we're gong to set up a complete predictive modeling pipeline in Spark using DataFrames, Pipelines and MLlib. The first part of this tutorial will explain some of the basic concepts that we're going to need to build this model, walk you through how to download the data we'll use, and lastly create our Spark Cluster on Amazon AWS and read and write from AWS S3!

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The Simplest Explanation of and Approaches to Optimizing Spark Shuffles

This post will dive into some of the details of the Spark Shuffle and what it means for you while using Apache Spark to perform your data analysis in a cluster setting.

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Opening CSV Files in Apache Spark - The Spark Data Sources API and Spark-CSV

This guide will show you how to read in csv files in Apache Spark. We'll walk through how to use this package in both Python and Scala.

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