Most data engineers chase shiny tools, but real growth comes from mastering the right resources. This isn’t just a list. It’s a curated arsenal that I would personally recommend to anyone serious about mastering modern Data Engineering in 2025. Let’s dive in 👇

Which real-time data platform led your transformation this year?. Let’s see what’s powering data engineering at scale! #DataEngineering2025.

Join millions who have switched to Grok.

Mastering DSA is non-negotiable if you want to ace coding interviews and level up as a developer. 🚀.

RAG pipeline monitoring is essential for reliable generative AI. 🔍. Regularly validate your LLM outputs using tools like Patronus AI and Galileo to detect hallucinations, bias, and errors early. These platforms enable continuous, real-time evaluation, ensuring your AI stays

RAG pipeline monitoring is critical for trustworthy AI don’t skip it. Regularly check the quality of your LLM’s output, not just the pipeline’s performance. Use specialized tools like Patronus AI and Galileo; they make it easier to spot errors and drift. Continuous evaluation.

The AI-native data stack is no longer a concept, it's the reality of 2025. Here’s why autonomous pipeline optimization and real-time observability are the non-negotiable foundation for scalable success:. 1️⃣Legacy data pipelines can’t keep up with the velocity & complexity of

10 Mistakes I Made as a Junior Data Engineer and How You Can Avoid Them!. When I started out, I obsessed over tools instead of fundamentals, ignored data quality until it was too late, and thought scaling = success. Each mistake taught me the hard way that principles > tools.

In 15 years of building data systems, I’ve seen one constant problem:. SQL transformations turning into unmanageable spaghetti. Hundreds of lines of copy-paste queries, no lineage, no testing…. We spent more time debugging dashboards than delivering insights. Then dbt arrived.

When I started out in tech, I kept hearing the same advice “Follow your passion.”. It sounded inspiring, but when I sat down in my first interview back in 2010, reality hit hard. My “passion” didn’t know how to debug SQL queries. My “passion” couldn’t explain object-oriented.

Want ACID guarantees, schema evolution & scalable ingestion in your data stack?. Here’s how Apache Iceberg powers a modern data architecture 🧵. 🔹 What is Iceberg?.An open-source table format (born at Netflix) for massive data lakes. It fixes Hive’s limits with fast metadata.

RT @datawithshash: Pro tip for Data Engineers 🚀. Stop hitting your lookup tables every single time. Cache them instead. ✅ Cuts join time d….

Pro tip for Data Engineers 🚀. Stop hitting your lookup tables every single time. Cache them instead. ✅ Cuts join time drastically.✅ Reduces compute costs by up to 3x.✅ Makes future maintenance way easier. Small tweak → Big performance boost. ⚡.#DataEngineering #BigData

💡Open-source keeps proving why it’s at the heart of modern data engineering. This week, these tools powered most of my workflows:.dbt → reliable SQL-based transformations, modular & maintainable. Great Expectations → data quality checks that actually scale. Apache Iceberg.

9 habits that changed my life (simple but powerful):. ✨ Weekends = family first. Evenings are non-negotiable → dinner & outings together. ✨ Sundays = movies, web series, and good food. ✨ Reading daily + learning from the internet. ✨ Currently upskilling in Data Science &

RT @paulabartabajo_: Behind every great data scientist. there is a greater data engineer.

AI is transforming observability and it's just getting started. Traditional monitoring shows you what’s wrong. AI-powered observability helps you understand why it’s wrong and even how to fix it. From anomaly detection → root cause analysis → smarter alerting, AI is turning.

Cloud Bytes — Episode 1 ☁️.(Tag someone who might find this helpful👇). Every Saturday, we’ll explore key cloud services powering modern Data Engineering. Let’s start with the workhorse of cloud-native data pipelines → Amazon S3 🚀. 🔹 What is Amazon S3?. An object storage

Master these 12 algorithms, and system design interviews will feel like solving puzzles instead of problems. 🧩.

Data Mesh & Data Fabric are two buzzwords you’ll hear a lot in 2025. But this is what do they really mean:. 👉 Data Mesh decentralizes data ownership. Each business domain manages its data like a product. Great for scale, but requires huge cultural shifts. 👉 Data Fabric takes

Everyone says “follow your passion.”. But here’s the truth no one tells you:.🔥 Passion fades under pressure. 💡 Skills compound and open doors. Your career isn’t built on excitement. It’s built on what you can do, build, and solve consistently. Passion is fuel. Skills are the.