Mastering Data Export with Python and SQLite

Most SQL developers know the basics. The top 1% know how to use SQL to answer questions a business actually cares about. 🎯 𝐇𝐞𝐫𝐞'𝐬 𝐲𝐨𝐮𝐫 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐐𝐋 𝐂𝐡𝐞𝐚𝐭𝐬𝐡𝐞𝐞𝐭 — 𝐭𝐡𝐞 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐭𝐡𝐚𝐭 𝐬𝐞𝐩𝐚𝐫𝐚𝐭𝐞 𝐠𝐨𝐨𝐝 𝐚𝐧𝐚𝐥𝐲𝐬𝐭𝐬 𝐟𝐫𝐨𝐦 𝐠𝐫𝐞𝐚𝐭 𝐨𝐧𝐞𝐬: ✅ Recursive CTEs — traverse hierarchies, org charts & category trees ✅ NTILE & PERCENT_RANK — rank and bucket your data like a pro ✅ ROWS BETWEEN — build sliding averages for any time window ✅ FIRST_VALUE — compare every row against the group's best ✅ Gaps & Islands — detect streaks and missing sequences in data ✅ Conditional Aggregation — pivot data without a PIVOT function Save this. You WILL need it. 💾 ♻️ Repost to help someone in your network level up their SQL. 📌 Follow Navya sri Kurapati🧑💻 for daily SQL, Python & Data content 🔗 Book a 1:1 mentorship session → https://lnkd.in/gfqXGEnq #SQL #AdvancedSQL #SQLTips #DataAnalytics #DataScience #DataEngineering #WindowFunctions #CTE #LearnSQL #DataAnalyst #TechCareer #SQLServer #Analytics #CareerGrowth #NavyaSriKurapati

🚀 Excited to share my recent learning on ETL (Extract, Transform, Load)! Over the past few days, I’ve been exploring how ETL plays a crucial role in data analytics by enabling efficient data integration from multiple sources. ETL involves extracting raw data, transforming it into a clean and structured format, and loading it into systems for analysis and reporting. I also gained hands-on understanding of how ETL processes are implemented using tools and technologies like Python, SQL, and Excel for data cleaning, transformation, and pipeline creation. This process is essential for ensuring data quality, consistency, and reliability in real-world analytics workflows. Looking forward to applying these concepts in building efficient data pipelines and deriving meaningful insights from data. #DataAnalytics #ETL #DataEngineering #Python #SQL #LearningJourney

Queries Within Queries: Mastering SQL Subqueries! 🧠🏗️ Day 68/100 Real-world questions are rarely simple. 🏗️ I’m on Day 68 of my #100DaysOfCode, and today I dove into Subqueries. In Data Science and Engineering, we often need to compare individual data points against a global benchmark. Instead of running two separate scripts, a subquery allows us to nest one query inside another for powerful, dynamic results. Technical Highlights: 🧠 Nested Logic: Writing 'Inner Queries' to calculate dynamic values (like the Global Average GPA) on the fly. 🎯 Dynamic Filtering: Using the output of a subquery as the condition for the 'Outer Query' ensuring results stay accurate as the database grows. ⚡ Algorithmic Efficiency: Reducing the 'Round-Trip' time between Python and the Database by letting SQL handle the complex comparisons internally. 🛡️ Data Integrity: Building reports that are always up-to-date without manual constant-value updates. Do check my GitHub repository here: https://lnkd.in/d9Yi9ZsC #SQL #Backend #100DaysOfCode #BTech #IILM #ComputerScience #AIML #Python #DatabaseArchitecture #SoftwareEngineering #LearningInPublic #WomenInTech

Struggling with advanced SQL? 🤔 Start learning Subqueries — one of the most powerful concepts in SQL 💻📊 A subquery lets you use one query inside another, making complex data analysis easier and smarter. 💡 Want to level up your SQL skills? This is a must-learn! 💬 Have you tried subqueries before? Comment “YES” or “NO” #SQL #Subquery #LearnSQL #DataAnalytics #DataScience #Database #TechSkills #Upskill #DataAnalyst #Coding #Programming #Students #CareerGrowth #Analytics #LearnTech #NattonTechnologies #NattonAI #NattonDigital #NattonSkillX

Struggling with advanced SQL? 🤔 Start learning Subqueries — one of the most powerful concepts in SQL 💻📊 A subquery lets you use one query inside another, making complex data analysis easier and smarter. 💡 Want to level up your SQL skills? This is a must-learn! 💬 Have you tried subqueries before? Comment “YES” or “NO” #SQL #Subquery #LearnSQL #DataAnalytics #DataScience #Database #TechSkills #Upskill #DataAnalyst #Coding #Programming #Students #CareerGrowth #Analytics #LearnTech #NattonTechnologies #NattonAI #NattonDigital #NattonSkillX

Struggling with advanced SQL? 🤔 Start learning Subqueries — one of the most powerful concepts in SQL 💻📊 A subquery lets you use one query inside another, making complex data analysis easier and smarter. 💡 Want to level up your SQL skills? This is a must-learn! 💬 Have you tried subqueries before? Comment “YES” or “NO” #SQL #Subquery #LearnSQL #DataAnalytics #DataScience #Database #TechSkills #Upskill #DataAnalyst #Coding #Programming #Students #CareerGrowth #Analytics #LearnTech #NattonTechnologies #NattonAI #NattonDigital #NattonSkillX

Struggling with advanced SQL? 🤔 Start learning Subqueries — one of the most powerful concepts in SQL 💻📊 A subquery lets you use one query inside another, making complex data analysis easier and smarter. 💡 Want to level up your SQL skills? This is a must-learn! 💬 Have you tried subqueries before? Comment “YES” or “NO” #SQL #Subquery #LearnSQL #DataAnalytics #DataScience #Database #TechSkills #Upskill #DataAnalyst #Coding #Programming #Students #CareerGrowth #Analytics #LearnTech #NattonTechnologies #NattonAI #NattonDigital #NattonSkillX

🚀 Built an End-to-End Data Pipeline using API & SQL Server! Excited to share my recent hands-on project where I built a complete data pipeline from scratch 👇 🔹 What I did: 1. Source Database (SQL Server)   ↓ 2. Create API using FastAPI   ↓ 3. Expose endpoint (/data)   ↓ 4. Call API using Python (requests)   ↓ 5. Get data in JSON format   ↓ 6. Connect to Target SQL Server   ↓ 7. Auto-create table (if not exists)   ↓ 8. Insert data into target table   ↓ 9. Verify data in SSMS 🔹 Tech Stack: Python | FastAPI | SQL Server | pyodbc | requests 🔹 Key Learnings: 💡 How APIs act as a bridge between systems   💡 Converting JSON data into structured format   💡 Building real-world ETL pipelines   💡 Automating data movement without manual intervention  This project helped me understand how real-world data engineering pipelines work — from data extraction to loading 🚀 Looking forward to building more such projects and improving my skills! #DataEngineering #Python #FastAPI #SQLServer #ETL #DataPipeline #LearningInPublic #100DaysOfData #BuildingInPublic

Most data analysts rely on SQL to handle data. It's simple, clean, and beginner-friendly. In fact, it's one of the most intuitive languages I've learned. However, SQL alone is inadequate and/or inconsistent for every data task (e.g., cleaning, transforming, or validating data). This is where Python thrives. So, today I decided to spend some time building a small Python workflow that filters, validates, and aggregates transaction data per account. Using Python objects and classes, I validated and transformed data types using conditional logic, handled errors with try/except, and filtered transactions by type to isolate relevant purchase activity for analysis. The SQL equivalent for this type of validation quickly becomes more complex and database-specific when handling inconsistent data types: --- SELECT account_id, SUM(CAST(amount AS FLOAT)), COUNT(*) FROM transactions WHERE type = 'purchase' AND TRY_CAST(amount AS FLOAT) IS NOT NULL AND TRY_CAST(amount AS FLOAT) >= 0 GROUP BY account_id; --- Many entry-level data analysts are comfortable using only SQL for analysis. But real-world data isn't always clean or consistent. That's why I focus on being an analyst who can take imperfect data, make it usable, and produce insights using the most effective tool. #DataAnalytics #Python #SQL #DataCleaning