PySpark SparkContext : – PySpark is the Python API for Apache Spark that allows you to leverage the simplicity of Python and the power of Apache Spark in order to manipulate big data. At the core of this functionality is the SparkContext, which is the entry point for Spark functionality. This guide will offer a …
SparkSession in PySpark : – The core of PySpark’s functionality is encapsulated in the `SparkSession` object, which serves as the entry point for programming Spark with the Dataset and DataFrame API. This article explores the `SparkSession` in PySpark, covering its creation, usage, and some of the key methods and configurations it offers. Introduction to SparkSession …
PySpark RDD Actions – An essential concept in Spark is the Resilient Distributed Dataset (RDD), which is a fundamental data structure of Spark. It is an immutable distributed collection of objects that can be processed in parallel across a cluster. Understanding RDD actions is crucial for leveraging the full potential of PySpark. What are PySpark …
PySpark DataFrame : – One of the most critical abstractions provided by PySpark is the DataFrame, which is a distributed collection of data organized into named columns. It is similar to a table in a relational database or a dataframe in pandas but with richer optimizations under the hood. PySpark DataFrames are designed to handle …
Identifying the data types of columns in a PySpark DataFrame is a crucial aspect of any data processing or analysis task. The data type of a column determines what kind of operations can be performed on it. Apache Spark, with its Python API – PySpark, provides easy-to-use functionalities to inspect the schema of a DataFrame, …
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Aliasing Columns in PySpark : – Aliasing is the process of renaming a dataframe column to a more readable or understandable name that makes sense in the context of your analysis or data processing pipeline. Understanding Aliasing in PySpark Aliasing columns can be particularly useful when the column names are generated dynamically by a computation, …
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PySpark flatMap Transformation : – One of the most useful transformations provided by PySpark is `flatMap`. Understanding this transformation and how to use it effectively is crucial for working with big data in Python. Understanding Transformations and Actions In PySpark, operations on RDDs (Resilient Distributed Datasets) can be broadly divided into two types: transformations and …
Concatenating columns in PySpark is a common data manipulation task that combines the data from two or more columns into a single column. This is especially useful when you want to merge text from different columns to create a more informative column or simply to prepare your data for further analysis. In this guide, we’ll …
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PySpark ArrayType Column : – One of the common data types used in PySpark is the ArrayType. This data type is useful when you need to work with columns that contain arrays (lists) of elements. In this guide, we will focus on working with ArrayType columns using PySpark, showcasing various operations and functions that can …
PySpark Shell Usage : – In this practical guide, we’ll explore how to use the PySpark shell, an interactive environment for running Spark commands, with helpful examples to get you started. Introduction to PySpark Shell The PySpark shell is an interactive Python environment that is configured to run with Apache Spark. It’s a tool for …
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