Welcome to the Future – AI Hints Today
Keyword is AI– This is your go-to space to ask questions, share programming tips, and engage with fellow coding enthusiasts. Whether you’re a beginner or an expert, our community is here to support your journey in coding. Dive into discussions on various programming languages, solve challenges, and exchange knowledge to enhance your skills.


SQL Tricky Conceptual Interview Questions
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 StepMethod / FunctionExample1. Remove DuplicatesROW_NUMBER(),…
Data Engineer Interview Questions Set4
Question:-“What really happens inside the Spark engine when I run a simple .read() or .join() on a file?” Let me break this down in a clear, interview-ready, cluster-level Spark execution flow, step-by-step: 🔍 Spark Cluster Background Process (Example: spark.read.csv(…)) Imagine this code: Let’s analyze it in chronological order: ✅ 1. Driver Program Starts the Spark…
Data Engineer Interview Questions Set3
This is a fantastic deep-dive! Let’s answer your question clearly and technically: ✅ Question Recap: If I read a 1 GB CSV file or a 1 GB Hive table into a DataFrame —❓ Does defaultParallelism apply?❓ How are tasks created and executed in this case? 🔧 Short Answer: No, defaultParallelism does not directly control how…
Data Engineer Interview Questions Set2
Advanced-level PySpark, Big Data systems, and backend engineering—here’s a breakdown of what questions you can expect, based on industry trends. ✅ Topic-wise Breakdown of Likely Questions 🔹 PySpark & Big Data (Core Focus) Area Sample Questions PySpark DataFrame APIs – How is selectExpr different from select?- Use withColumn, explode, filter in one chain.- Convert nested…
How SQL queries execute in a database, using a real query 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)…
Comprehensive guide to important Points and tricky conceptual issues in SQL
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…
RDD and Dataframes in PySpark- Code Snipppets
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…
Azure Databricks tutorial roadmap (Beginner → Advanced), tailored for Data Engineering interviews in India
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 Join Types- Syntax examples, Comparision
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)…
DataBricks Tutorial for Beginner to Advanced
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…
Complete crisp PySpark Interview Q&A Cheat Sheet
Certainly! Here’s the complete crisp PySpark Interview Q&A Cheat Sheet with all your questions so far, formatted consistently for flashcards, Excel, or cheat sheet use: Question Answer How do you handle schema mismatch when reading multiple JSON/Parquet files with different structures? Use .option(“mergeSchema”, “true”) when reading Parquet files; for JSON, unify schemas by selecting common…
Python Lists- how it is created, stored in memory, and how inbuilt methods work — including internal implementation details
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…
Data Engineer Interview Questions Set1
Explain a scenario on schema evolution in data pipelines Here’s an automated Python script using PySpark that performs schema evolution between two datasets (e.g., two Parquet files or DataFrames): ✅ Features: 🔧 Prerequisites: 🧠 Script: Schema Evolution Handler 🔍 Output: 💡 Notes: Automated script for schema evolution. first to check what fields are missing or…
PySpark SQL API Programming- How To, Approaches, Optimization
How the Python interpreter reads and processes a Python script and Memory Management in Python
I believe you read our Post https://www.hintstoday.com/i-did-python-coding-or-i-wrote-a-python-script-and-got-it-exected-so-what-it-means/. Before starting here kindly go through the Link. How the Python interpreter reads and processes a Python script The Python interpreter processes a script through several stages, each of which involves different components of the interpreter working together to execute the code. Here’s a detailed look at how…