HintsToday
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recent posts
- what APIs are, why they exist, and how we use them in Python?
- Python Strings- complete notes + interview Q&A
- Memory Management in PySpark- CPU Cores, executors, executor memory
- Memory Management in PySpark- Scenario 1, 2
- Develop and maintain CI/CD pipelines using GitHub for automated deployment, version control
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Category: Tutorials
Perfect — thanks for clarifying. Let’s build this from the ground up, starting with what APIs are, why they exist, and how we use them in Python. I’ll keep it thorough and structured but also student-friendly, so learners without prior HTTP or environment variable knowledge can follow smoothly. 🌐 APIs in Python — Beginner’s Guide…
Let’s go step by step and explain Python strings with beginner-friendly examples. 🔹 1. What is a String in Python? A string is a sequence of characters enclosed in single quotes (”), double quotes (“”), or triple quotes (”’ or “””). Strings are immutable → once created, they cannot be changed in place. 🔹 2.…
To determine the optimal number of CPU cores, executors, and executor memory for a PySpark job, several factors need to be considered, including the size and complexity of the job, the resources available in the cluster, and the nature of the data being processed. Here’s a general guide: 1. Number of CPU Cores per Executor 2. Number…
Suppose If i am given a maximum of 20 cores to run my data pipeline or ETL framework, i will need to strategically allocate and optimize resources to avoid performance issues, job failures, or SLA breaches. Here’s how you can accommodate within a 20-core limit, explained across key areas: 🔹 1. Optimize Spark Configurations Set…
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 +…
Here’s a complete guide to building and managing data workflows in Azure Data Factory (ADF) — covering pipelines, triggers, linked services, integration runtimes, and best practices for real-world deployment. 🏗️ 1. What Is Azure Data Factory (ADF)? ADF is a cloud-based ETL/ELT and orchestration service that lets you: 🔄 2. Core Components of ADF Component…
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) 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…
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…
This posts is a complete guide to Python OOP (Object-Oriented Programming) — both basic and advanced topics, interview-relevant insights, code examples, and a data engineering mini-project using Python OOP + PySpark. 🐍 Python OOP: Classes and Objects (Complete Guide) ✅ What is OOP? Object-Oriented Programming is a paradigm that organizes code into objects, which are…