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
Let’s directly compare Partitioning vs Bucketing in Spark from an optimization point of view. ✅ TL;DR Answer Purpose Best Choice Filtering / Scanning ✅ Partitioning Joining Large Tables ✅ Bucketing 🧠 Key Differences Feature Partitioning Bucketing Definition Splits data into directory-based partitions Splits data into fixed number of hash buckets Best For Optimizing filters and…
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 advanced tutorial covering: ✅ Concepts Explained 1. Medallion Architecture (Bronze → Silver → Gold) 2. Auto Loader vs Batch Load Auto Loader Batch Load Incremental file detection One-time or scheduled loads Uses cloud file notification Manual or cron-based Scalable & schema-evolving Static schema mostly 3. Databricks Workflows (Jobs) 4. CI/CD in Databricks…
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’s Post 5: Medallion Architecture with Delta Lake — the heart of scalable Lakehouse pipelines. This post is written in a tutorial/blog format with clear steps, diagrams, and hands-on examples for Databricks. 🪙 Post 5: Medallion Architecture with Delta Lake (Bronze → Silver → Gold) The Medallion Architecture is a layered data engineering design that…
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
🔍 Understanding cache() in PySpark: Functionality, Optimization & Best Use Cases 🔹 What is cache() in PySpark? 🔧 How Does cache() Work Internally? 🔹 How Does cache() Optimize Performance? ✅ Avoids Recomputations: ✅ Reduces IO Load & Network Latency: ✅ Speeds Up Iterative Jobs (ML, Graph Processing, Multiple Queries on Same Data): ✅ Optimizes Joins…
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…