10 Data Engineering Design Patterns to Know in 2025. From Lambda & Kappa to Data Mesh & Transactional Outbox this is your blueprint for scalable, real-time systems. Bonus: Trends & tips to stay future-ready. 👇 Dive in and level up.#DataEngineering #BigData #Tech

Most people confuse batch, micro-batch, and streaming pipelines. But understanding the difference is key to building the right data architecture. 🧵 Let’s break it down:. Batch:.Data is collected over time and processed at intervals. Great for reporting, BI, and offline

You might find it hard to speak with your manager, but always remember - “Manager bhi kabhi developer/fresher/intern tha”. So be truthful and always communicate transparently. No one is immune from failures or rejections in their career. Most people face this early on. I too.

Got this thoughtful review back in Oct 2024. It reminded me, most aspiring Data Engineers aren’t lacking talent. They’re lacking clarity, direction, and someone to talk to. I don’t offer magic. I just share what I’ve lived through wins, mistakes, and the mindset to grow. If

Switching to Data Engineering can feel overwhelming. There’s Python, SQL, Spark, Airflow, cloud platforms and everyone says “just build projects.”. But that only works if you know what to build and in what order. Here’s what actually works:. → Start with strong SQL and Python.

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Struggling to revise before your Data Engineering interview?. Here’s a quick 5-point revision checklist to stay sharp:. ✅ SQL Patterns (CTEs, Window Functions, Joins). ✅ Data Pipelines: Batch vs Streaming. ✅ System Design: Scaling, Latency, Trade-offs. ✅ Warehouse vs.

8️⃣One of the biggest mistakes beginners make? Forgetting to specify the ON condition properly. A missing or incorrect ON clause can give you a huge mess duplicate rows, wrong results, or even crash your query. Always double-check your JOIN logic and use table aliases to keep.

5️⃣FULL OUTER JOIN gives you the best of both worlds. It returns all rows from both tables and fills in NULLs where there's no match. It’s great for comparing datasets or identifying mismatches between two systems. 6️⃣CROSS JOINs give you every possible combination of rows between.

3️⃣LEFT JOINs return everything from the left table and match it with the right table where possible. If there’s no match, you’ll get NULLs on the right. It’s perfect when you want to include all users even the ones who didn’t place any orders. 4️⃣RIGHT JOINs are like LEFT JOINs.

1️⃣JOINs are how you bring together data from different tables. Imagine you have one table of users and another with their orders. JOINs help you link them based on a common field like user_id. The goal? Get one unified view that actually tells a story. 2️⃣The INNER JOIN is the.

🧵SQL JOINs explained the easy way – for aspiring Data Engineers. Most people memorize JOINs. Smart people visualize them. Let’s break them down using real-world examples and simple mental models 👇. #DataEngineering #SQL #ShashTalks.

RT @datawithshash: 10 Data Engineering Design Patterns to Know in 2025. From Lambda & Kappa to Data Mesh & Transactional Outbox this is you….

I used to think system design interviews were all about fancy tech knowledge. Turns out, clarity beats complexity every single time. If you can’t walk someone through your decisions why you chose X over Y, what trade-offs you made, how each component fits together then even the.

Here are 10 rules for securing your data pipelines because a fast pipeline is useless if it’s not secure 👇. 1. Encrypt data at rest and in transit. 2. Implement access controls (principle of least privilege). 3. Monitor logs and audit trails for unusual activity. 4. Regularly.

Your data pipeline isn’t complete without proper monitoring & alerting. One silent failure can cost millions or worse break trust. Here’s how to stay ahead using open-source tools:. - Use Prometheus + Grafana for real-time metrics.- Monitor DAGs & task failures with Airflow’s.

If you're working with Large Language Models, your #1 priority should be building a clean, reliable data pipeline. Here's what an end-to-end pipeline should cover:. ✅ Data collection from trusted sources.✅ Preprocessing & deduplication.✅ Removing noise & formatting.

It’s been a long time I haven’t talked about soft skills. Here’s what your leader looks at in your soft skills and why you must have them:. 🗣️ Communication - Bridge tech & business effectively.👥 Leadership - Inspire and guide your team.🧩 Problem-solving - Navigate complex.

Things I’ve learned as a leader (the hard way):. • You can’t make everyone happy and that’s okay. • Transparent, timely feedback is more powerful than waiting for review cycles. • A friendly, safe work culture isn’t optional. If your team fears rejection or can’t take a day.

Things I’ve learned as a leader (the hard way):. • You can’t make everyone happy and that’s okay. • Transparent, timely feedback is more powerful than waiting for review cycles. • A friendly, safe work culture isn’t optional. If your team fears rejection or can’t take a day.