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.
Memory Management in PySpark- CPU Cores, executors, executor memory
Analysis and Recommendations for Hardware Configuration and PySpark Setup Estimated Data Sizes CategoryRecords (crores)Monthly Size12-Month SizeTablesA80~8 GB~96 GBTablesB80~8 GB~96 GBTransaction Tables320~32 GB~384 GBSpecial Transaction100–200~10–20 GB~120–240 GBAgency Score150–450~15–45 GB~180–540 GB Total Estimated Data Volume: Hardware Configuration and PySpark Setup for ETL Jobs Step 1: Understanding the Data Volume TablesA (unique_id1-based):– 6 crores of unique_id1.– ~8 crores…
Memory Management in PySpark- Scenario 1, 2
how a senior-level Spark developer or data engineer should respond to the question “How would you process a 1 TB file in Spark?” — not with raw configs, but with systematic thinking and design trade-offs. Let’s build on your already excellent framework and address: ✅ Step 1: Ask Smart System-Design Questions Before diving into Spark configs, smart engineers ask questions to…
Develop and maintain CI/CD pipelines using GitHub for automated deployment, version control
Here’s a complete blueprint to help you develop and maintain CI/CD pipelines using GitHub for automated deployment, version control, and DevOps best practices in data engineering — particularly for Azure + Databricks + ADF projects. 🚀 PART 1: Develop & Maintain CI/CD Pipelines Using GitHub ✅ Technologies & Tools Tool Purpose GitHub Code repo +…
Complete guide to building and managing data workflows in Azure Data Factory (ADF)
Here’s a complete practical guide to integrate Azure Data Factory (ADF) with Unity Catalog (UC) in Azure Databricks. This enables secure, governed, and scalable data workflows that comply with enterprise data governance policies. ✅ Why Integrate ADF with Unity Catalog? Benefit Description 🔐 Centralized Governance Enforce data access using Unity Catalog policies 🧾 Audit &…
Complete guide to architecting and implementing data governance using Unity Catalog on Databricks
Here’s a complete guide to architecting and implementing data governance using Unity Catalog on Databricks — the unified governance layer designed to manage access, lineage, compliance, and auditing across all workspaces and data assets. ✅ Why Unity Catalog for Governance? Unity Catalog offers: Feature Purpose Centralized metadata Unified across all workspaces Fine-grained access control Table,…
Designing and developing scalable data pipelines using Azure Databricks and the Medallion Architecture (Bronze, Silver, Gold)
Designing and developing scalable data pipelines using Azure Databricks and the Medallion Architecture (Bronze, Silver, Gold) is a common and robust strategy for modern data engineering. Below is a complete practical guide, including: 🔷 1. What Is Medallion Architecture? The Medallion Architecture breaks a data pipeline into three stages: Layer Purpose Example Ops Bronze Raw…
Complete OOP interview questions set for Python — from basic to advanced
Here’s a complete OOP interview questions set for Python — from basic to advanced — with ✅ real-world relevance, 🧠 conceptual focus, and 🧪 coding triggers. You can practice or review these inline (Notion/blog-style ready). 🧠 Python OOP Interview Questions (With Hints) 🔹 Basic Level (Conceptual Clarity) 1. What is the difference between a class…
Classes and Objects in Python- Object Oriented Programming & A Data Engineering Project
Here’s a full Pytest project setup for Spark unit testing – focused on testing your ETL components like readers, transformers, writers, and even the full pipeline. ✅ 1. 📁 Project Structure ✅ 2. requirements.txt Install with: ✅ 3. conftest.py (Pytest Spark fixture) ✅ 4. test_reader.py ✅ 5. test_transformer.py ✅ 6. test_writer.py (for Delta write) ✅…
Parallel processing in Python—especially in data engineering and PySpark pipelines
Here’s a clear and concise breakdown of multiprocessing vs multithreading in Python, with differences, real-world data engineering use cases, and code illustrations. 🧠 Core Difference: Feature Multithreading Multiprocessing Concurrency Type I/O-bound CPU-bound Threads/Processes Multiple threads in the same process (share memory) Multiple processes (each with its own memory) GIL Impact Affected by Python’s GIL (Global…
All major PySpark data structures and types Discussed
🔍 What Does collect_list() Do in Spark SQL? collect_list() is an aggregation function in Spark SQL and PySpark. It: Collects all values of a column (within a group, if grouped) into a single array, preserving duplicates and order (non-deterministically). ✅ Syntax In PySpark: 🧾 Example Input table: category value A x A y A x…
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.…
Partition & Join Strategy in Pyspark- Scenario Based Questions
Great question! Understanding how Spark handles partitions in RDDs is fundamental to controlling performance, memory, and parallelism. Let’s break it down clearly. 🔹 What is a Partition in Spark? A partition is a logical chunk of an RDD’s data. Each partition is processed by one task on one core of an executor. ✅ How Spark…
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
pivoting (turning row values into columns) Got it — you mean pivoting (turning row values into columns). Here’s a quick breakdown depending on where you want to do it: 1️⃣ In SQL Use PIVOT or conditional aggregation. Example table: product month sales A Jan 100 A Feb 120 B Jan 90 B Feb 150 SQL…
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…