Krishna Chaitanya

🚨 Python Classes Made Easy. Full Course Out Now They struggle with classes. Not because they’re not smart But because OOP is often taught in a fragmented and confusing way. So I put together a 2+ hour deep dive on Python classes that walks through everything step by step, with a clear progression from basics to real-world application. Here’s exactly what’s inside: 🔹 Foundations ✔️ What Object-Oriented Programming actually is ✔️ How to create user-defined classes ✔️ Understanding self, instance attributes, and the __init__ method 🔹 Core Concepts (Where most people get stuck) ✔️ Class vs instance attributes (and why it matters) ✔️ Class methods vs static methods ✔️ Inheritance (broken down in two parts for clarity) ✔️ Abstraction and how it simplifies complexity 🔹 Intermediate to Advanced Topics ✔️ Operator overloading (with a full challenge + solution) ✔️ Polymorphism and how it applies in real code ✔️ Encapsulation, including getter and setter methods ✔️ Method overloading and how Python handles it 🔹 Real-World Application ✔️ Building a Sneaker Shop App to tie everything together The goal is simple: 👉 Help you move from copying code to actually understanding what you’re doing. You can watch it from start to finish or jump to specific sections depending on what you need. 🔑 If you’ve ever felt stuck with classes, this will help it finally click. Watch on YouTube here 👇 : youtu.be/LAiNEkggiU4?si…

CHESS websites for KIDS; chesskid.com kidchess.com chesstutor.org chesshere.com chesskids.org.uk learningchess.net chessfornovices.com

@Femiforge Highest

Which of the following is not a window function in SQL? 1. RANK() 2. ROW_NUMBER() 3. HIGHEST() 4. LEAD()

u can learn so much by being quiet and watching

Knowledge isn't free. You have to pay attention. ― Richard P. Feynman ✍️

Bruce Lee was right when he said: “The most dangerous person is the one who listens, thinks, and observes.”

> SQL Interview Questions Frequently Asked by Adobe (Beginner → Advanced) 1. BEGINNER (10 Questions): SQL Fundamentals + Product Analytics Thinking → Difference between WHERE, HAVING, and QUALIFY, how would Adobe expect you to use each in product analytics queries? → Explain INNER JOIN, LEFT JOIN, and CROSS JOIN using examples from user events, page views, or campaign data. → How does SQL handle NULLs in filters, joins, and aggregates, why this matters for Adobe Analytics data? → COUNT(*), COUNT(1), COUNT(column), what differences actually matter in large analytical datasets? → What happens when you run ORDER BY on large tables without LIMIT, why this is risky in Adobe-scale data? → UNION vs UNION ALL, when deduplication breaks funnel or attribution analysis? → Primary keys vs surrogate keys, which one fits better for event-based product data and why? → What is an index, and why columnar analytics systems (like those used at Adobe) rely less on them? → DELETE vs TRUNCATE, how does this differ in data warehouses vs transactional databases? → Normalization vs denormalization, when denormalized schemas are preferred for reporting and dashboards? 2. INTERMEDIATE (10 Questions): Analytics, Funnels & Business Metrics → Write a query to find the second highest metric per dimension (e.g., revenue per campaign). → Identify users with consecutive-day activity from Adobe-style event logs. → Rank pages, features, or campaigns using window functions. → GROUP BY vs window functions, when grouping destroys valuable user-level context. → Explain ROW_NUMBER, RANK, DENSE_RANK using A/B tests or feature adoption examples. → Detect duplicate events caused by retries or tracking issues. → Given daily user activity, find users active all 7 days of a week. → Correlated vs non-correlated subqueries, performance implications in analytical workloads. → How do you validate analytics queries to ensure dashboards and KPIs are trustworthy? → CTEs, when they improve readability vs when they silently hurt performance. 3. ADVANCED (10 Questions): Large-Scale Analytics & Data Engineering → Write a query to get top N items per category efficiently on billions of rows. → Detect missing or delayed events in time-series user interaction data. → Calculate retention, churn, and conversion funnels using pure SQL. → Design sessionization logic using timestamps and inactivity thresholds. → Compute rolling metrics (7-day averages, cumulative metrics) efficiently. → How does distributed SQL execution differ from traditional single-node databases? → Explain query optimization in columnar storage (predicate pushdown, partition pruning). → How do you handle data skew in joins at Adobe-scale event volumes? → OLTP vs OLAP, why Adobe products are fundamentally OLAP-driven. → How would you design tables for petabyte-scale analytics with frequent reads and evolving schemas?

As a Man please level up financially, the world is cruel to poor Men.

“The surface of the water is beautiful, but it is no good to sleep on” - African proverb Meaning: Don’t be fooled by superficial things, look deeper than the surface in all things so you can see the truth.

@Python_Dv 34

🤔🚀 Comment your answers below! 👇

- SQL + SELECT = Querying Data - SQL + JOIN = Data Integration - SQL + WHERE = Data Filtering - SQL + GROUP BY = Data Aggregation - SQL + ORDER BY = Data Sorting - SQL + UNION = Combining Queries - SQL + INSERT = Data Insertion - SQL + UPDATE = Data Modification - SQL + DELETE = Data Removal - SQL + CREATE TABLE = Database Design - SQL + ALTER TABLE = Schema Modification - SQL + DROP TABLE = Table Removal - SQL + INDEX = Query Optimization - SQL + VIEW = Virtual Tables - SQL + Subqueries = Nested Queries - SQL + Stored Procedures = Task Automation - SQL + Triggers = Automated Responses - SQL + CTE = Recursive Queries - SQL + Window Functions = Advanced Analytics - SQL + Transactions = Data Integrity - SQL + ACID Compliance = Reliable Operations - SQL + Data Warehousing = Large Data Management - SQL + ETL = Data Transformation - SQL + Partitioning = Big Data Management - SQL + Replication = High Availability - SQL + Sharding = Database Scaling - SQL + JSON = Semi-Structured Data - SQL + XML = Structured Data - SQL + Data Security = Data Protection - SQL + Performance Tuning = Query Efficiency - SQL + Data Governance = Data Quality

I am starting a new SQL course on YouTube from this Sunday, and this one is going to be different. IT professional with good SQL always have a opportunity to become a good Data Engineer. Back in 2022, I released a SQL playlist that went on to help millions of learners. I still receive messages from people telling me how that series helped them get their first job, switch to data roles, or clear interviews. Over the last few years, SQL interviews have become tougher. Expectations are higher. So this new course is much deeper than the 2022 series. Think of it as the 2X version. With this you will be prepared to crack the hardest of Interviews. Another reason for rebuilding the course is that the online IDE we used earlier no longer works. Many learners struggle while practising. This time I will ensure that everyone can practise smoothly without depending on tools that stop working midway. Whether you are a data engineer, data analyst, data scientist, or someone starting your IT journey, this course is for you. Let’s begin again, but with far more depth and clarity than before. Do support me in this initiative & share it with as many as you can. #sql #dataengineering #bigdata

After learning Data Analysis for 1 year. I tried applying for Jobs. And FAILED multiple times😢 15 LESSONS from my failed interviews:

@piyushgarg_dev Edge

Which browser do you use?

@codewithsu Backend

If you want to stay in Data Engineering for the next 10 years, be ready to solve complex problems. - The ones who’ll thrive in 2035 won’t just be “data movers”, they’ll be problem solvers. > Here’s what will separate them: • Scalability mindset → Building pipelines that don’t just work for 1GB of data, but for 1PB. • Data quality obsession → Designing systems that handle messy, incomplete, and inconsistent data without breaking. • Cost-awareness → Engineering solutions that are efficient, not just functional. • Tool-agnostic thinking → Spark, Flink, Snowflake, or something that doesn’t exist yet — principles > tools. • Automation & reliability → Monitoring, testing, and recovering from failures at scale. • Communication skills → Explaining trade-offs and architectures to teams that don’t speak “data engineering.” The complexity is coming whether we like it or not. Start solving harder problems today. Your future self will thank you.

Brace yourselves! It’s official: #Snowflake101 has started today! I’ve worked really hard on this series, can’t wait for you all to check it out.❄️ From Sept 15th, I’ll be posting daily at 11 AM, walking you through Snowflake step by step: from beginner to advanced. Plus, I have a special surprise for those who stick with the series till the end!