Don't Remove Duplicates Without Understanding Why

I fixed a data issue. And the metrics got worse. — Turns out… I had been double counting users. A small join issue. No errors. No failures. Just inflated numbers. — Fixing the data didn’t break the system. It exposed it. — That’s the tricky part with data engineering: Sometimes improving data quality makes everything look worse. #DataEngineering #DataQuality #SQL

Your data can pass NULL checks and still be completely wrong. Day-to-day data work can get messy. Even after removing/treating NULLs and duplicates, your dataset might still not be reliable. That’s where validity checks come in. Some errors happen earlier in the pipeline, so the data reaches your database with values that don’t make sense: ❌negative amounts ❌unrealistic ages 🚨inconsistent dates (like a churn date before registration) This kind of noise can distort your analysis and erode stakeholders’ trust. A simple way to catch these issues is using WHERE NOT. You define what should be true according to your business logic and flag everything that breaks it. It’s like saying: “If any of these conditions are not met, there’s a problem. Show me those rows.” What’s your go-to trick to validate data faster? Leave it in the comments 👇 📌 Found this useful? Save it for later. #SQLTips #DataAnalytics #DataScience #SQL #Analytics #BusinessIntelligence #DataEngineer #LearnSQL

Joins aren’t just about combining data - they’re also about filtering and understanding relationships. Welcome to Joins Part II - where we go beyond basic joins. - Left Semi Join Think of it as a filter. It returns rows from the left table only if a match exists in the right table - without bringing any columns from the right side. 👉 Useful when you just want to check: “Does this record exist in another dataset?” - Inner Join vs Left Semi • Inner Join → combines data from both tables • Left Semi → only filters the left table No duplicates, no extra columns — just existence check. - Left Anti Join The opposite of Left Semi. Returns rows from the left table that do NOT have a match in the right table. 👉 Useful for finding: • Missing records • Non-purchasers • Data mismatches - Cross Join Combines every row with every other row (Cartesian product). Powerful but expensive - use carefully. Key Takeaway: Not all joins are for merging data. Some are for filtering, validation, and finding gaps. ✍️ Content created by: Peer Grid Team 👥 Follow Peer Grid for simple data engineering explainers #PeerGrid #PySpark #BigData #DataEngineering #SQL #Joins

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

𝐒𝐭𝐢𝐥𝐥 𝐜𝐨𝐧𝐟𝐮𝐬𝐞𝐝 𝐚𝐛𝐨𝐮𝐭 𝐒𝐐𝐋 𝐉𝐎𝐈𝐍𝐬? 𝐘𝐨𝐮’𝐫𝐞 𝐧𝐨𝐭 𝐚𝐥𝐨𝐧𝐞. 👇 Most people learn joins… But very few actually visualize them. And that’s where things click. 💡 🔹 INNER JOIN → Only matching data 🔹 LEFT JOIN → Everything from left + matches 🔹 RIGHT JOIN → Everything from right + matches 🔹 FULL JOIN → Everything from both sides Simple rule: 👉 Think in terms of data inclusion, not syntax. Because in real-world data engineering… Joins decide whether your data is accurate or misleading. Save this. You’ll need it. 📌 Image Credits : Rocky Bhatia #SQL #DataEngineering #BigData #Analytics #LearnSQL #Databricks

🚀 Day 26/30 – SQL Challenge | Symmetric Pairs Today’s challenge was a really interesting one — finding symmetric pairs in a dataset. 🔍 What is a Symmetric Pair? Two rows are considered symmetric if: 👉 The first row’s X matches the second row’s Y 👉 And the first row’s Y matches the second row’s X In simple terms, pairs like (20, 21) and (21, 20) mirror each other. 💡 Key Learnings ✅ Understood how to compare rows within the same table ✅ Learned how to avoid duplicate outputs by maintaining order ✅ Handled tricky edge cases like pairs where both values are the same (e.g., 20,20) ✅ Improved logical thinking for real-world data relationships 📊 Sample Output • 20 20 • 20 21 • 22 23 🔥 This problem helped me realize how important data relationships and pairing logic are in real-world scenarios like matching transactions, network connections, and bidirectional mappings. #Day25 #30DaysSQLChallenge #SQL #LearningInPublic #HackerRank #Analytics

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

Working with Self-Joins in SQL Self-joins can be a bit tricky to understand at first, but they are incredibly powerful when you need to compare rows within the same table. Here’s a simple way to understand and use self-joins: A self-join is a regular join, but the table is joined with itself. Use Cases:   - Comparing Rows: Compare rows within the same table.   - Hierarchical Data: Query hierarchical data, such as organizational charts or family trees. Self-joins can be powerful tools for analyzing relationships within the same table. Experiment with self-joins to see how they can help you query your data more effectively. Here is a code snippet to help you understand how `Self-Join` works: 👇 Found this helpful? Repost it! 🔁 Follow Akash AB for Practical Data Engineering #sql #datascience #dataengineering #dataanalytics #selfjoin

🚀 I thought I understood data… until I realized I was calculating it wrong Early on, my approach was simple: If the query runs If the dashboard looks clean If the numbers seem consistent 👉 Then it must be correct Turns out, that’s a dangerous assumption. I came across a case where everything looked perfect — no missing data, no errors, clean trends. But the metric was still wrong. The issue? 👉 Aggregation at the wrong level Fixing that changed the number by ~16%. Same data. Completely different outcome. That’s when I realized: 👉 Data doesn’t fail loudly 👉 It fails silently And the scariest part? Most incorrect metrics still look correct. Since then, I’ve stopped just writing queries — and started questioning the logic behind them. Curious — what’s one mistake that changed how you look at data? #DataAnalytics #SQL #DataEngineering #AnalyticsEngineering #DataQuality #BusinessIntelligence #LearningInPublic

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