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.
PySpark Control Statements Vs Python Control Statements- Conditional, Loop, Exception Handling, UDFs
understanding when and why to use UDFs (User-Defined Functions) in PySpark is key for both real-world development and interviews. Let’s break it down clearly: ✅ What is a PySpark UDF? A UDF (User-Defined Function) lets you write custom logic in Python (or Java/Scala), which can then be applied to DataFrames just like native Spark functions.…
Pyspark Memory Management, Partition & Join Strategy – Scenario Based Questions
Great question — PySpark joins are a core interview topic, and understanding how they work, how to optimize them, and which join strategy is used by default shows your depth as a Spark developer. ✅ 1. Join Methods in PySpark PySpark provides the following join types: Join Type Description inner Only matching rows from both…
Data Engineer Interview Questions Set5
Here’s a detailed and interview-optimized answer sheet for each of your 8 questions (Q1–Q8), covering PySpark coding, Data Quality (DQ), SCD, optimization, and Spark architecture (AQE) — exactly how you’d want to respond in a technical interview: ✅ Q1. Extract dates from lines using string methods (not regex) and return those with year > 2018…
SQL Tricky Conceptual Interview Questions
Perfect—now I understand! You’re looking for tricky, high-quality SQL interview questions like: “What’s the difference between DELETE, DROP, and TRUNCATE?” These are concept-based, real-world, and interview-style—not just syntax exercises. 🔥 Top Tricky SQL Interview Questions (with Answers) Below is a carefully curated list covering real-world understanding, edge cases, performance, and design: ✅ 1. What is…
Data Engineer Interview Questions Set4
Perfect! Here’s everything inline, right in this window: ✅ Part 1: Spark Cluster Simulation Notebook (Inline Code) This Jupyter/Databricks notebook simulates how Spark behaves across cluster components: 🧠 Use .explain(True) at any step to inspect execution plan. ✅ Part 2: Spark Execution Flow — Mindmap Style Summary (Inline) ✅ Optional: Mindmap Format You Can Copy…
Data Engineer Interview Questions Set3
Let’s visualize how Spark schedules tasks when reading files (like CSV, Parquet, or from Hive), based on: ⚙️ Step-by-Step: How Spark Schedules Tasks from Files 🔹 Step 1: Spark reads file metadata When you call: 🔹 Step 2: Input Splits → Tasks File Size Block Size Input Splits Resulting Tasks 1 file, 1 GB 128…
Data Engineer Interview Questions Set2
How joins work internally, by default which join is used, which side bigger table should be used left or right, does it matter in spark Excellent question! Let’s break down how joins work internally in Spark, which is the default, and whether left/right side matters—especially from a performance perspective. 🔧 1. How Joins Work Internally…
What is Hive? Important Points, Interview Questions
Absolutely! Let’s break down the different Hive Table Types, their definitions, and the key differences with practical examples and a comparison table. 🧠 Hive Tables and Their Types Hive provides logical abstraction over data in HDFS or compatible storage systems. There are 4 major types of Hive tables: 1️⃣ Managed Tables (Internal Tables) ✅ What…
How SQL queries execute in a database, using a real query example.
We should combine both perspectives—the logical flow (SQL-level) and the system-level architecture (engine internals)—into a comprehensive, step-by-step guide on how SQL queries execute in a database, using a real query example. 🧠 How a SQL Query Executes (Combined Explanation) ✅ Example Query: This query goes through the following four high-level stages, each containing deeper substeps.…
Comprehensive guide to important Points and tricky conceptual issues in SQL
Let me explain why NOT IN can give incorrect results in SQL/Spark SQL when NULL is involved, and why LEFT ANTI JOIN is preferred in such cases—with an example. 🔥 Problem: NOT IN + NULL = Unexpected behavior In SQL, when you write: This behaves differently if any value in last_week.user_id is NULL. ❌ What…