Data Quality Issue Exposed by Fix

I used to think duplicates in a dataset were just something to clean up. Add a DISTINCT, move on, problem solved. But that approach started to feel wrong after a while. In most cases, those “duplicates” weren’t random. They were coming from how the data was structured or how it was being joined. Multiple rows often meant something real was happening in the data. A one-to-many relationship. Changes over time. Records that were valid in different contexts. Using DISTINCT made the output look cleaner, but it also removed that context. I’ve seen cases where the numbers looked correct after removing duplicates, but the underlying issue in the logic was still there. Over time, I’ve started treating duplicates less as something to remove and more as something to understand. That shift in thinking took some time. Once you understand why the extra rows exist, the right solution becomes clearer. Sometimes it’s fixing the join. Sometimes it’s selecting the right record. Sometimes it’s aggregating correctly. But it’s rarely just filtering rows out. #DataEngineering #SQL #DataQuality

Yesterday started with a simple message: “Why are customer numbers higher than usual?” The query had run perfectly. No errors. No failures. No red flags. At first glance, everything looked normal. But the numbers didn’t feel right. So instead of jumping into rewriting the SQL, I paused and checked the data behind it. That’s when I found the real issue: A join key that looked unique… wasn’t unique at all. One customer was matching multiple rows in another table, quietly inflating the final count. The query wasn’t broken. The relationship between the tables was. I checked: • Key uniqueness • Source table grain • Duplicate records • Expected one-to-one / One-to-many logic After fixing that, the numbers aligned immediately. Moments like this remind me: Many SQL problems don’t come from syntax. They come from assumptions. Before changing the query, understand the data story first. #SQL #DataEngineering #Analytics #ETL #DataQuality #Learning #Tech

𝐓𝐡𝐢𝐬 𝐭𝐚𝐛𝐥𝐞 𝐝𝐞𝐬𝐢𝐠𝐧 𝐦𝐢𝐬𝐭𝐚𝐤𝐞 𝐬𝐢𝐥𝐞𝐧𝐭𝐥𝐲 𝐜𝐨𝐫𝐫𝐮𝐩𝐭𝐬 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚. Most people don’t notice it at first. Because everything seems to work. 𝐔𝐧𝐭𝐢𝐥 𝐭𝐡𝐢𝐬 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 👇 • Same customer stored multiple times • Small typo = different person • Updates don’t match across rows And suddenly… 👉 Your data is inconsistent 👉 Your analysis is unreliable This is not a SQL problem. It’s a design problem. 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐞𝐫𝐞 𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐜𝐨𝐦𝐞𝐬 𝐢𝐧: • Removes duplication • Creates a single source of truth • Makes updates predictable 𝐁𝐮𝐭 𝐡𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐩𝐚𝐫𝐭 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐢𝐠𝐧𝐨𝐫𝐞 👇 👉 More normalization = more joins And more joins = more complexity 💡 𝐑𝐞𝐚𝐥 𝐬𝐤𝐢𝐥𝐥 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐢𝐧𝐠… It’s knowing how far to go. 🤔 𝐐𝐮𝐢𝐜𝐤 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: Have you ever faced issues because of duplicate or messy data? 📌 Save this — you’ll need it in real projects. — Navya sri Kurapati🧑💻 𝐂𝐚𝐫𝐞𝐞𝐫 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞: https://lnkd.in/gfqXGEnq #SQL #DataAnalytics #DatabaseDesign #LearnSQL #DataEngineering #Analytics #DataModeling #TechCareers #DataCommunity

Most SQL mistakes don’t come from syntax.They come from not understanding the data. One thing I learned early while working on customer datasets at scale: Before writing ANY complex SQL… I always spend 5 to 10 minutes just understanding the data. Here’s my quick checklist: 👉 SELECT COUNT(*) → How big is the dataset? 👉 SELECT COUNT(DISTINCT customer_id) → Unique entities 👉 GROUP BY key columns → Any unexpected duplicates? 👉 WHERE column IS NULL → Missing data check 👉 LIMIT 10 → Sanity check rows It sounds basic, but this habit has saved me from: • incorrect aggregations • duplicate joins • wrong business conclusions • and painfully long debugging sessions In real-world analytics, wrong insights are worse than no insights. Clean thinking > complex queries. Curious , what’s one SQL habit that saved you time? #SQL #DataAnalytics #DataScienceTips #AnalyticsLife #DataEngineering #LearnSQL #WomenInTech #DataCareer

🚀 Big Data Made Simple: Structured, Semi-Structured & Unstructured Explained Big Data is classified into 3 main types: 📊 Structured Data Organized data in rows & columns (e.g., Excel, SQL databases, banking records) 🧩 Semi-Structured Data Has partial structure using tags (e.g., JSON, XML, emails) 🌐 Unstructured Data No fixed format (e.g., images, videos, social media posts, PDFs) 💡 Each type needs different tools for storage and analysis, making it crucial in Data Analytics & Data Engineering. #BigData #DataScience #DataEngineering #Analytics #MachineLearning #TechBasics

Sharp and Relatable: ⚙️ Small mistakes in data engineering… big consequences in business. A missing filter. A wrong join. A duplicate load. Looks small, right? But it can lead to: 📉 Wrong reports 📉 Bad decisions 📉 Lost trust 💡 In data engineering, details aren’t small; they are everything. ✔️ Validate your data ✔️ Double-check your logic ✔️ Never assume correctness 🚀 Because one small bug can impact thousands of decisions. What’s the smallest mistake that caused you the biggest issue? 👇 #DataEngineering #DataQuality #BigData #ETL #TechCareers #Analytics

𝗠𝗼𝘀𝘁 𝗦𝗤𝗟 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗵𝗮𝗽𝗽𝗲𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗲𝘃𝗲𝗻 𝘀𝘁𝗮𝗿𝘁𝘀. 𝗔𝗻𝗱 𝘁𝗵𝗲 𝗳𝗶𝘅 𝗶𝘀 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝗼𝗻𝗲 𝘄𝗼𝗿𝗱: 𝙒𝙃𝙀𝙍𝙀. If you want clean data, relevant insights, and fewer messy outputs, you need to master filtering. The 𝘞𝘏𝘌𝘙𝘌 clause helps you keep the right rows and remove the noise. Here’s why that matters:  • It isolates the data you actually care about  • It removes irrelevant records early  • It makes your analysis faster and more accurate  • It is the foundation of data clean-up Why is this so critical? Because real-world data is noisy. 𝗚𝗼𝗼𝗱 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝗱𝗼 𝗻𝗼𝘁 𝘀𝘁𝗮𝗿𝘁 𝗯𝘆 𝗮𝗻𝗮𝗹𝘆𝘇𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴.  1. They start by filtering what matters.  2. That is how you turn raw tables into useful answers. CTA: What’s the first SQL command you learned that actually changed how you worked with data? #SQL #DataAnalytics #DataCleaning #DataAnalyst #LearnSQL

STOP using DISTINCT blindly. You’re losing control over your data. 🔹 Duplicates sneak into your data more often than you think: 👉 joins gone wrong 👉late-arriving data 👉retries in pipelines 👉messy ingestion 🔹 Most people fix it like this: SELECT DISTINCT id, name, city FROM customers; It works… But it’s also the least controlled approach. ✅ Here are 3 ways to remove duplicates in SQL: 1. ROW_NUMBER() (Best Way) full control over which row stays lets you keep latest / priority records production-safe 2. DISTINCT (Quick Fix) simple and fast removes exact duplicates ⚠️ no control over which row survives 3. GROUP BY (Same as DISTINCT) does the same job more verbose rarely needed for deduplication 🎯 REAL TAKEAWAY All 3 give the same result. But only one gives control. If you care about data correctness, use ROW_NUMBER() — not luck. 🔥 WHY THIS MATTERS Choosing the wrong method can: hide data issues break downstream reports silently corrupt logic And the worst part? You won’t notice until it’s too late. Which one do you use most in your projects? 🤔 #SQL #DataEngineering #AnalyticsEngineering #DataEngineer #ETL #Databricks #BigData

🤯 Ever noticed this in your data? Two tables. Same structure. But one behaves predictably… the other causes chaos. Here’s why 👇 ⸻ 🟢 Table 1: Primary Key Every record has a Primary Key ✔️ No duplicates ✔️ No NULLs ✔️ Always uniquely identifiable 💡 Think of it like an Aadhaar number — one person, one identity. No confusion. ⸻ 🟡 Table 2: Unique Key Uses a Unique Key ✔️ No duplicates (good) ⚠️ Allows NULLs Sounds fine… until you realize: 👉 Multiple NULLs = multiple “unknowns” And that’s where subtle bugs creep in 🐛 ⸻ 🚨 Real-World Impact Choosing the wrong key can break: ❌ Joins (unexpected mismatches) ❌ Deduplication logic ❌ Reporting accuracy ⸻ 🎯 Simple Takeaway Primary Key = Strict identity 🔒 Unique Key = Flexible uniqueness 🔓 ⸻ 💡 Pro Tip for Data Engineers If your data needs guaranteed identity → Primary Key If you need optional uniqueness → Unique Key (handle NULLs carefully!) ⸻ Full Credit - Shilpa Das Thank you so much for sharing this beautiful insight 💬 Question for you Have you ever faced a bug because of NULLs in a Unique Key? ⸻ 🔥 Hashtags #DataEngineering #SQL #DataModeling #DatabaseDesign #ETL #DataQuality #BigData #Analytics #DataEngineer #TechInterview #LearningInPublic #DataArchitecture #CareerGrowth #CloudData #DataTips #Debugging #RealWorldProblems

Data quality isn’t just about fixing bad data it’s about preventing bad decisions. 📊⚡ From null checks to schema drift detection, these are some of the essential validations every Data Engineer should know to build reliable pipelines and trustworthy data systems. Clean data = confident business decisions. 🚀 #DataEngineering #DataQuality #ETL #BigData #DataPipeline #SQL #PySpark #DataAnalytics #DataValidation #DataEngineerJobs #TechCareer #LearningJourney