Boost Data Quality with 5 Python Scripts

Stop loading massive datasets into memory and crashing your pipeline. 🛑 I used to load multi-gigabyte CSVs into Pandas, only to watch my memory usage spike to 100% and trigger an OOM kill. Switching to Python generators transformed how we handle large-scale data ingestion. Before (messy): import pandas as pd data = pd.read_csv("large_file.csv") for row in data.itertuples(): process(row) After (clean): import pandas as pd def stream_data(file_path): for chunk in pd.read_csv(file_path, chunksize=10000): yield from chunk.itertuples() for row in stream_data("large_file.csv"): process(row) Why this matters for data engineers: By processing data in chunks rather than loading the entire file, you keep your memory footprint constant regardless of file size. This allows your small containers to handle massive files without crashing. What is your go-to method for memory-efficient data processing in Python? #DataEngineering #Python #BigData #DataPipelines #SoftwareEngineering

🚀 Stop killing your CPU with Python loops. I recently refactored a data transformation pipeline that was crawling because it processed 5 million rows using a standard row-by-row iteration. Moving from native loops to vectorized operations changed everything. Before optimisation: results = [] for i in range(len(df)): val = df.iloc[i]['price'] * df.iloc[i]['tax_rate'] results.append(val) df['total'] = results After optimisation: df['total'] = df['price'] * df['tax_rate'] Performance gain: 45x faster execution time. Vectorization offloads the heavy lifting to highly optimised C code under the hood. When you use Pandas or NumPy native methods, you stop fighting the interpreter and start leveraging memory alignment. If you are still writing loops for data manipulation, you are leaving massive amounts of compute time on the table. It is the easiest performance win you can claim this week. What is the biggest speed boost you have ever achieved by swapping a loop for a built-in vectorised function? #DataEngineering #Python #Pandas #Performance #Optimization

I just killed 1,904 MB of RAM bloat with 40 lines of C. 🚀 I was testing Python's standard json.loads() on a 500MB log file today. 🛑 The Result: 3.20 seconds of lag and a massive 1.9GB RAM spike. For a high-scale data pipeline, that’s not just "slow"—that’s a massive AWS bill and a system crash waiting to happen. So, I built a bridge. By offloading the heavy lifting to the metal using Memory Mapping (mmap) and C pointer arithmetic, I created the Axiom-JSON engine. ✅ Standard Python: 3.20s | 1,904 MB RAM ✅ Axiom-JSON (C-Bridge): 0.28s | ~0 MB RAM That is an $11\times$ speedup and near-perfect memory efficiency. Stop throwing more RAM at your problems. Start writing better architecture. CTA: If your data pipelines are hitting a performance wall, DM me. I’m looking to help 2 teams optimize their compute costs this week. #SystemsArchitecture #Python #CProgramming #PerformanceEngineering #DataEngineering #CloudOptimization

Why I am paranoid about schema drift. When you build a pipeline like Shop Pulse, everything works perfectly as long as the data looks exactly like you expect. But in the real world, someone always changes a column name or a data type without telling you. That is why I have been focusing on schema enforcement. If a bad record hits your Spark job and you haven't handled it, the whole pipeline crashes at 3 AM. I’ve started implementing validation layers that catch these changes before they pollute the Delta Lake. It is more work upfront, but it is the only way to sleep peacefully knowing your data is actually reliable. #DataEngineering #ApacheSpark #DataQuality #Python #BackendDevelopment

I stopped writing boilerplate backend code manually. Here’s what my workflow looks like now: • Use AI to generate Spring Boot controllers and DTOs • Refine and enforce structure manually • Add tests immediately to validate behavior • Integrate into CI/CD before merging For Python scripts: • Use AI to scaffold data pipelines • Focus my time on edge cases and correctness The real gain isn’t just speed. It’s consistency. Less time rewriting the same patterns More time thinking about system design What’s one repetitive task you’d automate if you could? #AIinEngineering #DeveloperTools #Productivity #Automation #SoftwareEngineering #DevWorkflow #BuildInPublic

One lesson that keeps coming up in my data analytics journey: the right data structure can outperform the most advanced algorithm 🧠 Python dictionaries have been a game-changer for me in real-time scenarios—especially for caching intermediate results and tracking session-level data 🔄 What makes them powerful?  Constant-time lookups ⚡  Flexible structure for dynamic data 🔀  Easy integration into pipelines 🔧 When you’re working with streaming or high-volume data, these advantages add up quickly 📈 It’s not always about doing more—it’s about doing things smarter 💡 What data structure do you rely on the most? #DataAnalytics #Python #DataStructures #RealTimeSystems #BigData #LearningInPublic #TechThoughts

Day 6/10 🚀 This is where your data starts to take shape. Collections — the backbone of every Python program. Without the right one? Slower code, messy logic. With the right one? Faster lookups, cleaner design. 📋 What I covered today: 01 → Lists — slicing & comprehensions 02 → Tuples — immutability & unpacking 03 → Dictionaries — CRUD & O(1) lookup 04 → Sets — unique values & operations 05 → Frozenset 06 → Advanced — defaultdict, Counter, namedtuple 07 → Iterators — iter() & next() 08 → Mini Project — Inventory Management System Built a simple system using dictionaries to manage stock & pricing — a real-world pattern used in inventory and data pipelines. Day 1 ✅ Day 2 ✅ Day 3 ✅ Day 4 ✅ Day 5 ✅ Day 6 ✅ 4 more to go. Drop a 🐍 if you’ve ever used a list when a set would’ve been better 😄 #Python #Collections #DataEngineering #LearningInPublic #CleanCode #10DaysOfPython #DataStructures

📢 ⚡ 𝐒𝐜𝐡𝐞𝐦𝐚 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐝𝐨𝐧’𝐭 𝐛𝐫𝐞𝐚𝐤 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬… 𝐭𝐡𝐞𝐲 𝐛𝐫𝐞𝐚𝐤 𝐭𝐫𝐮𝐬𝐭. 👉 A pipeline was running perfectly for months. 👉 No failures. No alerts. Everything looked stable. 🤕 But one day, business numbers didn’t match reality. 📍 After digging in, we found the issue: ✔️ A column type changed upstream - string to integer. ✔️ No error. No crash. ✔️ Just silently incorrect aggregations. 👉 That’s when we realized: ✔️ Schema changes don’t break pipelines… they break trust. #DataEngineering #SchemaEvolution #DataQuality #BigData #DataPipelines #DataArchitecture #ETL #AnalyticsEngineering #Spark #pyspark #python #schema #data

Your data science notebook just became an RCE vector. Marimo — an open-source Python notebook used by data teams everywhere — had a pre-authentication remote code execution vulnerability