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
You’re absolutely right to challenge that — and this is an important subtlety in PySpark that often gets misunderstood, even in interviews. Let’s clear it up with precision: ✅ Clarifying the Statement: “You cannot use Python for loops on a PySpark DataFrame” That statement is partially true but needs nuance. ✅ 1. What You Cannot…
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
Great! You’re absolutely right that compressed columnar formats like Parquet and ORC are preferred in Spark for performance, schema awareness, and column pruning. Let’s answer your question: ✅ Q: “How do I enable compression when writing files in Spark (Parquet/ORC)?” Spark does not compress Parquet data by default, but you can easily enable it by…
SQL Tricky Conceptual Interview Questions
Great question! In SQL, DELETE, TRUNCATE, and DROP are used to remove data—but they work very differently in terms of what they remove, speed, rollback, and usage. Here’s a quick comparison followed by detailed explanations with examples: 🔍 Quick Comparison Feature DELETE TRUNCATE DROP What it removes Rows All rows Entire table (structure + data)…
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
Great question! Understanding the difference between a UDF (User Defined Function) and built-in Spark SQL functions is crucial for writing performant PySpark code. 🔍 UDF vs In-built Spark Function Feature UDF (User Defined Function) In-built Spark Function Definition A custom function defined by the user to extend Spark’s capabilities Predefined, optimized functions provided by Spark…
What is Hive? Important Points, Interview Questions
Yes, you’re absolutely right to explore this distinction. ✅ Hive does support ACID properties, starting from Hive 0.14+ (full features in Hive 3.x). ❌ Vanilla Apache Spark (especially before Delta Lake or Hudi/Iceberg) does NOT support ACID natively. Let’s break this down clearly: ✅ Hive with ACID – YES Apache Hive supports ACID transactions for…
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…