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Category: Tutorials
PySpark Control Statements Vs Python Control Statements- Conditional, Loop, Exception Handling, UDFs
Python control statements like if-else can still be used in PySpark when they are applied in the context of driver-side logic, not in DataFrame operations themselves. Here’s how the logic works in your example: Understanding Driver-Side Logic in PySpark Breakdown of Your Example This if-else statement works because it is evaluated on the driver (the main control point of…
Q1.–We are working with large datasets in PySpark, such as joining a 30GB table with a 1TB table or Various Transformation on 30 GB Data, we have 100 cores limit to use per user , what can be best configuration and Optimization strategy to use in pyspark ? will 100 cores are enough or should…
Data cleaning in SQL is a crucial step in data preprocessing, especially when working with real-world messy datasets. Below is a structured breakdown of SQL data cleaning steps, methods, functions, and complex use cases you can apply in real projects or interviews. ✅ Common SQL Data Cleaning Steps & Methods Step Method / Function Example…
Here’s a clearer, interactive, and logically structured version of your Oracle SQL Query Flow explanation with real-world analogies, step-by-step breakdowns, diagrams (as text), and a cross-engine comparison with MySQL and SQL Server (MSSQL). We’ve also added a crisp SQL optimization guide. 🧠 How an SQL Query Flows Through Oracle Engine (with Comparison and Optimization Tips)…
Here’s a comprehensive guide to important and tricky conceptual issues in SQL, including NULL behavior, joins, filters, grouping, ordering, and subqueries. ✅ 1. NULLs: The #1 source of confusion a. NULL ≠ NULL b. NOT IN with NULL c. Arithmetic with NULL ✅ 2. JOIN Issues a. INNER JOIN drops unmatched rows. b. LEFT JOIN…
Where to Use Python Traditional Coding in PySpark Scripts Using traditional Python coding in a PySpark script is common and beneficial for handling tasks that are not inherently distributed or do not involve large-scale data processing. Integrating Python with a PySpark script in a modular way ensures that different responsibilities are clearly separated and the…
Here’s a complete Azure Databricks tutorial roadmap (Beginner → Advanced), tailored for Data Engineering interviews in India, including key concepts, technical terms, use cases, and interview Q&A: ✅ What is Azure Databricks? Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for the Microsoft Azure cloud. 🔗 How Azure Databricks integrates…
Spark SQL supports several types of joins, each suited to different use cases. Below is a detailed explanation of each join type, including syntax examples and comparisons. Types of Joins in Spark SQL 1. Inner Join An inner join returns only the rows that have matching values in both tables. Syntax: Example: 2. Left (Outer)…
Absolutely! Let’s break down Data Lake, Data Warehouse, and then show how they combine into a Data Lakehouse Architecture—with key differences and when to use what. 🧊 1. Data Lake vs Data Warehouse Feature 🪣 Data Lake 🏛️ Data Warehouse Type of Data Raw, unstructured, semi-structured, structured (e.g., logs, images, JSON, CSV, Parquet) Structured data…
In Python, a list is a mutable, ordered collection of items. Let’s break down how it is created, stored in memory, and how inbuilt methods work — including internal implementation details. 🔹 1. Creating a List 🔹 2. How Python List is Stored in Memory Python lists are implemented as dynamic arrays (not linked lists…