SQL Views as Query Functions

In this lecture, we break down one of the most important SQL topics: joins and relational database design. You will learn how tables connect through primary keys and foreign keys, and how to confidently use INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN to answer real data questions. We also cover relationship types (one-to-one, one-to-many, many-to-many), normalization basics, and common mistakes that cause duplicate or missing results. By the end of this session, you should be able to: Design cleaner relational schemas Choose the right join for a problem Write join queries that are accurate and readable Debug join issues in real-world datasets Perfect for beginners in SQL, data analysis, and backend development. If this helped, like the video, subscribe, and share it with someone learning SQL. #SQL #DatabaseDesign #SQLJoins #DataAnalytics #PostgreSQL #LearnSQL #RelationalDatabase https://lnkd.in/g5JDMHYa

(10-04-2026)Architecture & Constraints Data Types, Create Table, and Constraints Day 2 of SQL, and today was all about structure. In Python, you can be a bit flexible with variables. In SQL? Not so much. Rules are everything. I spent the day learning how to build tables properly. It turns out, telling a database exactly what kind of data to expect is the secret to keeping it from breaking later. The Highlights: Data Types: Understanding the nuance between INT, VARCHAR, and DECIMAL. It’s all about efficiency. Constraints: I learned how to use NOT NULL, UNIQUE, and DEFAULT to make sure only "good" data gets into my tables. Insert and Alter Command Primary Keys: Every row needs an identity. Learning how to set a Primary Key felt like giving every piece of data its own unique SSN. It’s a different way of thinking—building the "container" perfectly before you even put a single row of data inside. #SQLLearning #DataStructure #MySQL #BackendDevelopment #Day2

The last two mssql-python releases shipped big features: Bulk Copy in 1.4 for high-throughput data loading, and Apache Arrow in 1.5 for zero-copy analytics. Version 1.6 is about what happens next: you take those features into production, scale up your thread pool, and find out where the driver was quietly holding you back. https://lnkd.in/d-snaHPW

A senior developer looked at my SQL query and said: "It works. But it will kill the database in production." I had HackerRank Advanced SQL. Passed every test. Felt good about it. Turns out I had been testing my memory, not my understanding. He pointed at three things: My correlated subquery was running once per row. Not once total. On 50 million rows, that's 50 million subqueries. I had wrapped the column in a function. The index existed. It was doing nothing. I had never run EXPLAIN before shipping a single query to production. I didn't argue. I just felt stupid for a while. Then I went and fixed my mental model. The gap nobody talks about: certifications test whether you know SQL. Production tests whether you know what breaks SQL. These are not the same exams. Saved this cheat sheet for anyone who is still on the wrong exam: → Window functions instead of correlated subqueries → CTEs instead of nested subqueries — easier to read, easier to debug → EXISTS instead of NOT IN—NOT IN silently returns zero rows when the subquery has NULLs → Never wrap an indexed column in a function → EXPLAIN before every query that goes anywhere near production Save this. #SQL #DataEngineering #DataAnalytics #Python #Learning

500M records. Python vs. SQL. Who wins? In the world of high-volume data loading, Python is great for logic, but SQL is King for throughput. Here’s the technical breakdown: 1. The "Middleman" Tax: Python requires row-by-row object conversion. SQL skips the overhead and moves data directly. 2. Protocol Efficiency: SQL bulk tools bypass the "chatty" nature of ODBC/JDBC drivers. 3. Page-Level Loading: SQL moves data from disk to pages without bloating system RAM. 4. Log Optimization: Native tools allow for minimal logging, whereas Python-driven inserts often choke the transaction log. If you’re moving billions of rows, don’t build a bridge with Python. Use the native highway built into your database. What's your go-to tool for massive migrations? BCP, COPY, or a custom Spark job? #DataStrategy #TechLeadership #DatabaseEngineering #Python #SQL #PostgreSql #SqlServer #Oracle #Mysql #Spark

From Microsoft Community Hub, mssql-python 1.6: Unblocking Your Threads, by DavidLevy "The last two mssql-python releases shipped big features: Bulk Copy in 1.4 for high-throughput data loading, and Apache Arrow in 1.5 for zero-copy analytics. Version..." https://lnkd.in/eTsQM4dU

You aren't going to believe it - we're still not done! Hot on the heels of the ODBC 18.6.2 release, we're shipping mssql-python 1.5, the next update to Microsoft's official Python driver for SQL Server, Azure SQL, and SQL database in Fabric. The headline feature: Apache Arrow fetch support. You can now pull query results directly into pandas, Polars, or any Arrow-native framework with zero-copy data transfer from the C++ layer. If you're building data pipelines or doing analytics against SQL Server, this is a big one. Also in this release: - sql_variant type support with automatic Python type resolution - Native UUID/GUID objects (no more manual string conversion) - Fixes for qmark parameter detection, credential caching, and bulk copy auth A huge thank you to community contributor Felix Graßl for building the Arrow integration from the ground up. Try it now: pip install --upgrade mssql-python Blog post with full details and code examples: https://lnkd.in/gmeZVA6X #SQLServer #AzureSQL #Python #ApacheArrow #DataEngineering #DataDrivers #Microsoft

A senior developer looked at my SQL query and said: ‘It works. But it will fail in production.’ I had written what I thought was a solid query. It passed every test. It returned the correct output. It even handled edge cases. Then came the review. “This will scan the entire table. On real data, it won’t finish.” That moment changed how I think about SQL. I had been optimizing for correctness. Production systems require optimization for scale and efficiency. What I learned the hard way: → A correlated subquery inside a WHERE clause can destroy performance → NOT IN behaves unpredictably with NULLs → Indexes become useless when columns are wrapped in functions → EXPLAIN PLAN is not optional—it's the starting point Most SQL problems are not logic problems. They are execution problems. That’s the gap between writing queries… and writing queries that survive production. If you’ve worked with large datasets, you’ve seen this happen. #SQL #DataEngineering #DataAnalytics #Learning #Python

SQL or pandas, the tool is secondary. 💡 The logic is what matters. A classic use case: employees earning above their department average. 👉 SQL ,using a CTE: WITH avg_salary AS ( SELECT department, AVG(salary) AS dept_avg FROM employees GROUP BY department ) SELECT e.name, e.salary, a.dept_avg FROM employees e JOIN avg_salary a ON e.department = a.department WHERE e.salary > a.dept_avg; 👉 pandas, same logic: avg_salary = ( employees .groupby("department")["salary"] .mean() .reset_index(name="dept_avg") ) result = employees.merge(avg_salary, on="department") result = result[result["salary"] > result["dept_avg"]] ###Same pattern. Different syntax. 🟢 aggregate by group 🟢 join back to original dataset 🟢 filter using group-level context This is what defines data work across tools. Not memorizing syntax but recognizing reusable patterns. 😊 Master the logic. The syntax will follow. #SQL #Python #Pandas #DataEngineering #DataScience

Sharing a glimpse of my hands-on practice with MySQL! 💻 I've been working on the Sakila database, exploring various queries like: Concatenating fields to create custom columns. Filtering data using specific WHERE conditions. Using Pattern Matching (LIKE) for flexible searching. Calculating Distinct counts to understand data distribution. Har query ke saath SQL ke liye mera confidence aur badhta ja raha hai. Small steps everyday! 🚀 can I post daily about Python,SQL and EXCEL? pls comment if you have post on it. #SQL #MySQL #DataAnalysis #LearningEveryday #MySQLWorkbench