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Category: Tutorials
Optimization in PySpark is crucial for improving the performance and efficiency of data processing jobs, especially when dealing with large-scale datasets. Spark provides several techniques and best practices to optimize the execution of PySpark applications. Before going into Optimization stuff why don’t we go through from start-when you starts executing a pyspark script via spark…
Error and Exception Handling: Python uses exceptions to handle errors that occur during program execution. There are two main ways to handle exceptions: 1. try-except Block: 2. Raising Exceptions: Logging Errors to a Table: Here’s how you can integrate exception handling with logging to a database table: 1. Choose a Logging Library: Popular options include:…
What is Hadoop? Hadoop is an open-source, distributed computing framework that allows for the processing and storage of large datasets across a cluster of computers. It was created by Doug Cutting and Mike Cafarella and is now maintained by the Apache Software Foundation. History of Hadoop Hadoop was inspired by Google’s MapReduce and Google File…
String manipulation is a common task in data processing. PySpark provides a variety of built-in functions for manipulating string columns in DataFrames. Below, we explore some of the most useful string manipulation functions and demonstrate how to use them with examples. Common String Manipulation Functions Example Usage 1. Concatenation Syntax: 2. Substring Extraction Syntax: 3.…
✅ What is a DataFrame in PySpark? A DataFrame in PySpark is a distributed collection of data organized into named columns, similar to a table in a relational database or a Pandas DataFrame. It is built on top of RDDs and provides: 📊 DataFrame = RDD + Schema Under the hood: So while RDD is…