Pyspark -Introduction, Components, Compared With Hadoop

PySpark is a powerful Python API for Apache Spark, a distributed computing framework that enables large-scale data processing. Spark was initially started by Matei Zaharia at UC Berkeley’s AMPLab in 2009, and open sourced in 2010 under a BSD license. In 2013,...

PySpark Architecture- (Driver- Executor) , Web Interface

PySpark, as part of the Apache Spark ecosystem, follows a master-slave architecture(Or Driver- Executor Architecture) and provides a structured approach to distributed data processing. Here’s a breakdown of the PySpark architecture with diagrams to illustrate...

What is Hive?

Hive a Data warehouse infra Hive is an open-source data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. It allows users to query and manage large datasets residing in distributed storage using a SQL-like language...

Pyspark- Jobs , Stages and Tasks explained

In PySpark, jobs, stages, and tasks are fundamental concepts that define how Spark executes distributed data processing tasks across a cluster. Understanding these concepts will help you optimize your Spark jobs and debug issues more effectively. Let’s break down how...

String Data Manipulation and Data Cleaning in Pyspark

In PySpark, string manipulation and data cleaning are essential tasks for preparing data for analysis. PySpark provides several built-in functions for handling string operations efficiently on large datasets. Here’s a guide on how to perform common string manipulation...

Mysql or Pyspark SQL query- The placement of subqueries

Let’s list all possible places where subqueries in MySQL or Hive QL or Pyspark SQL Query can be used: 1. In the SELECT Clause Subqueries can compute a value for each row. SELECT employee_id, (SELECT COUNT(*) FROM project_assignments pa WHERE pa.employee_id =...