Apache Spark - Complete guide

Getting Started

Welcome to our complete guide to Apache Spark! In this blog post, we will introduce you to Apache Spark, a powerful open-source data processing engine that is designed to be fast and flexible. We will start by discussing the origins of Spark and why it has become so popular in recent years. From there, we will dive into the key features and capabilities of Spark, including its support for real-time stream processing and machine learning. We will also provide a detailed walkthrough of how to get started with Spark, including how to install and set up a development environment. By the end of this guide, you will have a solid understanding of what Apache Spark is and how you can use it to build powerful data-driven applications.

What is Apache Spark

If you’re interested in working with big data, you’ve probably heard of Spark - but you might not be entirely sure what it is or how it works. In this post, we’ll give you a complete introduction to Apache Spark. We’ll start by explaining exactly what Spark is and how it differs from other big data technologies. Then, we’ll dive into the key features and capabilities of Spark, including its support for real-time stream processing and machine learning.

What is Apache Spark

Setup Apache Spark Locally (PySpark)

We will make Apache Spark setup a breeze. Check out our article to begin writing Spark Code locally

Setup Apache Spark

Fetching data with Apache Spark (PySpark)

The first step to using Apache Spark is of course, to fetch some data! We’re going to look at the few most common methods

Read / write CSV files with Apache Spark (PySpark)

We’re going to read csv files to continue with the course. The files are included in this post on how to read csv files.

Reading and writing CSV Files with PySpark

Data Manipulation

We’re now going to dive into all the common ways to manipulate data within a PySpark dataframe

Renaming Columns With PySpark