Sam Miraki’s Post

One of my data pipelines broke… and I had to figure out why. At first, it was frustrating. Data wasn’t reaching the final table. Dashboards weren’t updating. No clear error message. Instead of guessing, I followed a step-by-step approach: 👉 Step 1: Check the source Is data coming in correctly? 👉 Step 2: Verify ingestion Did the pipeline actually pick up the data? 👉 Step 3: Inspect transformations Any logic breaking in processing? 👉 Step 4: Check the output layer Is data written correctly to the destination? 👉 Step 5: Look at logs Find the exact point of failure That’s when I found it… A small issue in one transformation step was breaking the entire pipeline. Fixing it was easy. Finding it was the hard part. That experience taught me: 👉 Debugging is not about guessing… it’s about isolating. Now whenever something breaks, I don’t panic. I just follow the flow. How do you debug pipeline failures? #DataEngineering #DataPipeline #Debugging #BigData #ETL #DataEngineer #TechLearning #ProblemSolving #CloudComputing #Analytics #DeveloperJourney #LearnInPublic #CareerGrowth

Salam everyone! Data engineering is not about big data. It is about not losing data. Everything else is secondary. Here is a simple checklist I use before any pipeline runs: If this runs twice, do I get the same result? If it crashes at row 10,000, do I restart from 0 or from 10,001? If the source has nulls, does my pipeline break or handle it? Can I explain what this pipeline does in two sentences to someone non-technical? Four questions. No fancy tools required. Get these right first. Add complexity later. #DataEngineering #DataFundamentals #SimpleOverComplex Wasalam!

“We just need one more column…” The most expensive sentence in Data Engineering 💀 It sounds simple, right? Just add a column. That’s it. But here’s what actually happens behind the scenes 👇 👉 12 pipelines break 👉 Dashboards start showing wrong data 👉 Backfilling turns into a nightmare 👉 Schema conflicts everywhere 👉 And suddenly… you’re debugging at 2 AM All because of… one column. ⚠️ Reality check: In data engineering, nothing is isolated. Every small change has a ripple effect across the entire system. 💡 What I’ve learned: ✔ Always think end-to-end impact ✔ Version your schemas ✔ Build resilient pipelines ✔ Communicate changes early Because in real life… “It’s never just one column.” If you’ve faced this, you know the pain. Drop a 🔥 if this hit you hard. #DataEngineering #BigData #ETL #DataPipeline #TechReality #EngineeringLife #Analytics #DataQuality

🎯 𝗢𝗻𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝘁𝗵𝗮𝘁 𝗺𝗮𝗱𝗲 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝗰𝗹𝗶𝗰𝗸 𝗳𝗼𝗿 𝗺𝗲 ... 🎯 👉 𝗠𝗲𝗱𝗮𝗹𝗹𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Before this, I used to: ❌ Clean data randomly ❌ Build directly on raw tables Now I think in layers: 🥉 𝗥𝗮𝘄 → untouched 🥈 𝗖𝗹𝗲𝗮𝗻 → structured 🥇 𝗚𝗼𝗹𝗱 → business-ready Simple idea. Huge clarity. 👉 𝘋𝘢𝘵𝘢 𝘪𝘴 𝘯𝘰𝘵 𝘤𝘭𝘦𝘢𝘯𝘦𝘥 𝘰𝘯𝘤𝘦… 𝘪𝘵’𝘴 𝘳𝘦𝘧𝘪𝘯𝘦𝘥 𝘴𝘵𝘦𝘱 𝘣𝘺 𝘴𝘵𝘦𝘱. If you're learning data engineering, don’t skip this. It will save you hours of confusion. #DataEngineering #DataArchitecture #LearningInPublic

A green checkmark on a pipeline doesn’t mean your data is correct. It just means the code didn’t crash. Here’s how I explain Data Observability👇 ✈️ The Pilot’s “Silent” Dashboard You’re flying at 30,000 feet. Every light on the dashboard is green. Engines? ✅ Fuel? ✅ Navigation? ✅ But then you look out the window… The wing is on fire 🔥 The system didn’t fail. It just wasn’t watching the right things. 📊 The “Green Pipeline, Red Data” problem Your ETL job succeeds. No errors. No alerts. But the output? * 1 million rows of zeros * Missing data * Broken metrics Everything ran… but nothing is right. 👁️ The 5 Pillars of Data Observability It’s not just “Did it run?” — it’s “Is it healthy?” * Freshness: Is the data up to date? * Distribution: Does it look normal? * Volume: Did we get what we expected? * Schema: Did anything silently change? * Lineage: If it’s broken, where did it start? 💡 The mindset shift Monitoring asks: “Is the system working?” Observability asks: “Is the system healthy—and why not?” One is reactive. The other is resilient. 🧭 The takeaway Don’t wait for an angry Slack message to tell you something’s wrong. Build systems that catch the fire before the dashboard turns green. 👉 What’s your “wing on fire” story? Ever had a pipeline succeed… and still deliver chaos? #DataEngineering #DataQuality #Observability #CloudInfrastructure

#DataQuality issues don’t always break pipelines. They quietly break decisions. 📉 Everything can look fine — Pipelines running, dashboards refreshing… But the numbers still don’t add up. That’s what makes data quality tricky. From what I’ve seen: • Missing values → misleading trends • Inconsistent definitions → confusion across teams • Late data → incorrect reporting • Silent issues → hardest to detect It made me realize — Reliability isn’t just pipelines running. It's data being trusted. Still learning how to build systems where data is not just available, but reliable. What’s the most common data quality issue you’ve faced? #DataEngineering #DataQuality #DataAnalytics #DataPipelines #SQL #DataReliability #DataPlatform #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

One thing I learned while studying Data Engineering: 👉 Theory alone doesn’t prepare you for real-world problems. When I started building pipelines, I realized: - Data is messy - Systems fail - Requirements change And that’s where real learning happens. Now I focus on: ✔️ Building end-to-end pipelines ✔️ Handling errors and edge cases ✔️ Thinking like a business, not just a developer That shift changed everything. #DataEngineering #DataPipelines #TechSkills #Analytic

The hardest part of data engineering is not technical It’s deciding: what NOT to build Early on, it’s tempting to: • track every event • store everything • build flexible pipelines for “future use” But more data ≠ more value Over time, this leads to: ❌ noisy datasets ❌ unclear priorities ❌ slower systems Strong data work is about: ✔ selecting the right data ✔ defining clear use-cases ✔ saying “no” to unnecessary complexity Because every extra table, field, or pipeline is: something you’ll have to maintain later Good engineers don’t just build more. They build less, but better. #DataEngineering #Analytics #DataStrategy #Backend #SoftwareEngineering

Your PIPELINE passed! But your DATA lied ⚠️ Bad data costs the average org $12.9–15M a year — and up to 25% of revenue in some sectors. Every data engineer secretly knows: there’s no “quality” without a clear hierarchy. Not a checklist. A chain. Break one link, everything downstream lies. Here's the order that actually matters 👇 1️⃣ Structure — Is the shape right?(Schema/Duplicates) 2️⃣ Completeness — Did it all show up? (Nulls/SLA) 3️⃣ Value validity — Is the content real? (Ranges/Regex) 4️⃣ Cross-field logic — Do fields agree? (Start Date < End Date) 5️⃣ Statistical health — Is it behaving? (Distributions/Outliers) Most engineers start at 3. That's the problem. Fix it at ingestion: costs 1x. At the dashboard: costs 100x. Dataversity That's not a metaphor — that's the 1x10x100 rule, and it's why sequence is architecture, not preference. Here's the hierarchy to explore for every data engineer to follow while building "QUALITY" data solutions! Which layer is currently the "silent killer" in your pipelines? Let’s swap horror stories in the comments! 👇 #data #engineering #bigdata #dataquality #analytics