In PySpark, a DataFrame is a distributed collection of data organized into named columns, similar to a table in a relational database or an Excel spreadsheet. DataFrames provide a powerful abstraction for working with structured data, offering ease of use, high-level transformations, and optimization features like catalyst and Tungsten. This article will cover how to […]
Tag: pyspark
PySpark | How to Create a RDD?
Resilient Distributed Datasets (RDDs) are the core abstraction in PySpark, offering fault-tolerant, distributed data structures that can be operated on in parallel. Although the DataFrame API is more popular due to its higher-level abstractions, RDDs are still fundamental for certain low-level operations and are the building blocks of PySpark.
In this article, you’ll learn how to create RDDs in PySpark, the different ways to create them, and when you should use RDDs over DataFrames.
PySpark | How to Create a Spark Session?
Creating a Spark session is the first step when working with PySpark, as it allows you to interact with Spark’s core functionality. This article will walk you through the process of creating a Spark session in PySpark.
PySpark | How to setup PySpark on a Windows Machine?
In this post, we will extend that setup to include PySpark, allowing you to work with Spark using Python. Let’s dive into the steps to get PySpark running on your Windows machine!