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.
what APIs are, why they exist, and how we use them in Python?
🔹 1. Reading Apps in Python When you say “read apps”, I assume you mean: In Python, “apps” often means either: 🔹 2. Reading Data from S3 & Oracle in Python ✅ Reading from AWS S3 ✅ Reading from Oracle Database You can use cx_Oracle or oracledb package. 🔹 3. Creating a Simple API in…
Python Strings- complete notes + interview Q&A
Perfect 👌 — let’s build a complete notes + interview Q&A sheet for Python strings covering: 📝 Python String Notes (for Interviews) 1. String Basics 2. Common String Methods Method Description Example .upper() Convert to uppercase “abc”.upper() → “ABC” .lower() Convert to lowercase “ABC”.lower() → “abc” .title() Capitalize each word “hello world”.title() → “Hello World”…
Memory Management in PySpark- CPU Cores, executors, executor memory
Analysis and Recommendations for Hardware Configuration and PySpark Setup Estimated Data Sizes Category Records (crores) Monthly Size 12-Month Size TablesA 80 ~8 GB ~96 GB TablesB 80 ~8 GB ~96 GB Transaction Tables 320 ~32 GB ~384 GB Special Transaction 100–200 ~10–20 GB ~120–240 GB Agency Score 150–450 ~15–45 GB ~180–540 GB Total Estimated Data…
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
You’re asking for a full PySpark OOP-based ETL framework: ✅ Full working PySpark project template (inline)✅ Add SQL transformation step using a metadata table✅ Add parallel file loading and dependency handling Let’s build this step-by-step, all inline and complete. 🔥 PROJECT: OOP-Based Metadata-Driven ETL Framework (PySpark) 🎯 Goal: ✅ 1. SIMULATED METADATA TABLE We’ll simulate…
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
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…
Partition & Join Strategy in Pyspark- Scenario Based Questions
repartition() and coalesce() can help resolve OOM (Out of Memory) errors in Spark — but how you use them depends on the root cause of the memory pressure. Let’s break it down clearly. 🧠 First: What causes OOM in Spark? OOM (OutOfMemoryError) in Spark usually happens when: ✅ How repartition() and coalesce() help Function Purpose…
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…