TroubleShoot Pyspark Issues- Error Handling in Pyspark

When working with PySpark, there are several common issues that developers face. These issues can arise from different aspects such as memory management, performance bottlenecks, data skewness, configurations, and resource contention. Here’s a guide on troubleshooting...

Partitioning a Table in SQL , Hive QL, Spark SQL

Partitioning in SQL, HiveQL, and Spark SQL is a technique used to divide large tables into smaller, more manageable pieces or partitions. These partitions are based on a column (or multiple columns) and help improve query performance, especially when dealing with...

Oracle Query Execution phases- How query flows?

SQL query flows through the Oracle engine in the following steps: Step 1: Parsing The SQL query is parsed to check syntax and semantics. The parser breaks the query into smaller components, such as keywords, identifiers, and literals. Step 2: Optimization The parsed...

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 =...

Lesson 3: Data Preprocessing

Data preprocessing is a crucial step in machine learning. It involves cleaning and transforming raw data into a format suitable for modeling. Data Cleaning Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in the data such as...

Lesson 2: Python for Machine Learning

In this lesson, we’ll cover essential Python libraries for machine learning: NumPy, Pandas, Matplotlib, and Scikit-Learn. NumPy NumPy is a library for numerical computations in Python. It provides support for arrays, matrices, and many mathematical functions....

Lesson 1: Introduction to AI and ML

What is AI? Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI systems can perform tasks such as visual perception, speech recognition, decision-making, and language translation. What...

I am Learning AI & ML

My Posts in this series will follow below said topics. Introduction to AI and ML What is AI? What is Machine Learning? Types of Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Key Terminologies Python for Machine Learning Introduction...

Optimizations in Pyspark:- Explain with Examples

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...