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.
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 SizeBlock SizeInput SplitsResulting Tasks1 file, 1 GB128 MB88 tasks (Stage 0)10 files,…
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…
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…
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 your complete tutorial on Apache Spark DataFrame in Azure Databricks, covering everything from basics to advanced operations exclusive to Azure Databricks (not available in standard on-prem PySpark setups). 📘 Azure Databricks DataFrame Tutorial (2025) 📌 Part 1: What is a DataFrame in Spark? A DataFrame is a distributed, table-like, high-level API for structured and…
Spark SQL Join Types- Syntax examples, Comparision
Here are Spark SQL join questions that are complex, interview-oriented, and hands-on — each with sample data and expected output to test real-world logic. ✅ Setup: Sample DataFrames 🔹 Employee Table (emp) 🔹 Department Table (dept) 🧠 1. Find all employees, including those without a department. Show department name as Unknown if not available. 🧩…
DataBricks Tutorial for Beginner to Advanced
Here is Post 2: Cloud Setup for Databricks (Azure & AWS) — written in a tutorial/blog format. It’s detailed, comparative, fact-based, interactive, and use-case-driven. Ideal for your Databricks Beginner → Advanced Series. 🚀 Post 2: Cloud Setup for Databricks (Azure & AWS) — A Comparative Guide for Data Engineers Welcome to the second post in…
Complete crisp PySpark Interview Q&A Cheat Sheet
🔍 What Are Accumulators in PySpark? Accumulators are write‑only shared variables that executors can only add to, while the driver can read their aggregated value after an action completes. Feature Detail Purpose Collect side‑effect statistics (counters, sums) during distributed computation Visibility Executors: can add()Driver: can read result (only reliable after an action) Data types Built‑ins: LongAccumulator, DoubleAccumulator,…
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
1.Tell us about Hadoop Components, Architecture, Data Processing 2.Tell us about Apache Hive Components, Architecture, Step by Step Execution 3.In How many ways pyspark script can be executed? Detailed explanation 4.Adaptive Query Execution (AQE) in Apache Spark- Explain with example 5.DAG Scheduler in Spark: Detailed Explanation, How it is involved at architecture Level 6.Differences between…
PySpark SQL API Programming- How To, Approaches, Optimization
🔧 Optimizing Repartitioning & Minimizing Shuffling in PySpark Repartitioning is essential in distributed computing to optimize parallel execution, but excessive shuffling can degrade performance. Here’s how to handle it efficiently: 🔹 1️⃣ Understanding Repartitioning Methods 1. repartition(n) – Increases parallelism but causes full shuffle ✔ Use Case: When load balancing is needed (e.g., skewed data).❌…
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…
Lists and Tuples in Python – List and Tuple Comprehension, Usecases
Absolutely. Here is a complete list of coding questions focused only on list and tuple in Python, covering all levels of difficulty (beginner, intermediate, advanced) — ideal for interviews: ✅ Python List and Tuple Coding Interview Questions (100+ Total) 🔰 Beginner (Basic Operations) 🧩 Intermediate (Logic + Built-in functions) 💡 Problem Solving (Application) 🧠 Advanced…
How to Solve a Coding Problem in Python? Step to Step Guide?
🔹 Pattern Matching Techniques in Problem-Solving (Python & Algorithms) 📌 What is Pattern Matching in Coding? Pattern Matching in problem-solving refers to recognizing similarities between a given problem and previously solved problems, allowing you to reuse known solutions with slight modifications. Instead of solving every problem from scratch, identify its “type” and apply an optimized…