DBT invented Analytics Engineers, not real roles

Data Engineering — what people see vs what it really is 👩💻 ☑️ Most people think data engineering is just about writing SQL queries, building dashboards, or connecting APIs. But that’s only the surface. Underneath lies the real work designing data architecture, creating and managing complex pipelines, optimizing storage, ensuring data quality, and constant monitoring. ☑️ Data engineers don’t just move data they build the entire system that keeps businesses running on trusted insights. #DataEngineering #BigData #DataPipelines #ETL #DataArchitecture #Analytics #CloudData #TechCommunity

Data engineering isn’t just about moving data from A to B. It’s about building the pipelines, warehouses, and dashboards that turn raw numbers into decisions. Every query optimized, every ETL flow streamlined, brings businesses closer to clarity.

As data continues to grow exponentially, the role of a Big Data Engineer has become the backbone of every data-driven organization. I wrote this article to simplify what it truly means to be a Big Data Engineer — not just about handling terabytes of information, but about turning raw data into real business value. In this piece, I’ve broken down the 5 V’s of Big Data (Volume, Velocity, Variety, Veracity, and Value) with practical, real-world examples — making it easier for aspiring engineers and business leaders to understand why this role is so crucial in modern enterprises. If you’ve ever wondered “what does a Data Engineer actually do behind the scenes?”, this article is for you.

Over the years, I’ve seen data teams struggle to build platforms from scratch. Strong engineers, but mismatched skill sets. Some were great at building reliable ingestion pipelines, others at modeling business logic, and bridging the two was always hard. I recently co-authored an article with Ashok Singamaneni for The New Stack on the topic, "Platform vs. Analytics Engineer: Why Your Team Needs Both": https://lnkd.in/eh6dtCQP The good news is that with Databricks, the Bronze layer is largely solved. With #Lakebase, the gap between OLTP and OLAP is reducing quickly, allowing data teams to focus more on the Gold layer and analytics. This shift will also simplify how leaders think about hiring and team structure in the future.

Learn the crucial difference between platform and analytics engineers for a successful data platform. By Ashok Singamaneni and Gaurav Nanda (Databricks)

Why Your Data Pipeline Is Slower Than It Should Be: 5 Common Bottlenecks > We build data pipelines to move fast — but often end up debugging slow DAGs, late dashboards, and mysterious lags. > After working across multiple data stacks, here are 5 common performance bottlenecks I keep seeing (and how to fix them): --- 🔹 1. Poor Data Modeling Flat tables and nested JSONs may work early on, but don’t scale. Use dimensional modeling (star/snowflake) or data vault for complex systems. Clean schemas = faster queries. --- 🔹 2. Overloaded Transformation Layers If your dbt or Airflow DAG has 30+ transformations, ask: → Can I simplify? → Is this logic better upstream (e.g., in ingestion)? Complex logic costs compute and clarity. --- 🔹 3. Lack of Partitioning or Clustering Reading entire datasets is a silent killer. 👉 Use date/time partitioning, clustering keys, or Z-ordering (in Delta/Iceberg) to limit scan ranges. --- 🔹 4. Unoptimized SQL / Spark Jobs Nested subqueries, cross joins, and SELECT * will slow you down. 💡 Run EXPLAIN plans, cache where it helps, and monitor execution time regularly. --- 🔹 5. No Observability or Monitoring If your pipeline fails silently or lags go unnoticed, you can’t fix what you can’t see. Tools like Great Expectations, Monte Carlo, or OpenLineage can help catch issues early. ✅ Pro Tip: Start small. Even fixing one of these can save hours of processing time and make your analytics 10x more reliable. 💬 I’d love to hear from others — What’s the worst performance bottleneck you’ve ever faced in a data pipeline? Let’s make pipelines faster, together. 🚀 #DataEngineering #DataPipelines #AnalyticsEngineering #ModernDataStack #dbt #BigData #ETL #ELT #DataOps #ApacheSpark #Databricks #ReverseELT #DataEngineeringinAi

One of the most common questions asked in Data Engineering interviews is: 👉 “What are Delta Tables or Delta Lake?” If you’re preparing for roles in Databricks, Spark, or modern data platforms, chances are you’ll definitely face Delta-related questions. From my own experience, while appearing for interviews, I noticed that Delta Lake is a hot topic, and here are the Top 20 Delta Table questions I frequently came across: 1️⃣ What is Delta Lake and how is it different from a traditional data lake? 2️⃣ What are Delta Tables and why do we use them? 3️⃣ How does Delta Lake ensure ACID transactions? 4️⃣ Difference between Managed and External Delta Tables. 5️⃣ How Delta Lake handles schema evolution? 6️⃣ What is Time Travel in Delta Tables and how do you use it? 7️⃣ Explain Delta Log and its importance. 8️⃣ How Delta Lake supports concurrent reads/writes? 9️⃣ Difference between Delta Tables and Parquet. 🔟 What are CDC (Change Data Capture) capabilities in Delta Lake? 1️⃣1️⃣ How do you optimize Delta Tables for performance? 1️⃣2️⃣ What is the role of Z-Ordering in Delta Tables? 1️⃣3️⃣ How Delta Lake handles deletes and updates (MERGE operations)? 1️⃣4️⃣ Delta Lake file compaction and vacuuming – why is it needed? 1️⃣5️⃣ Difference between Delta Live Tables and Delta Tables. 1️⃣6️⃣ How Delta Tables integrate with Spark SQL? 1️⃣7️⃣ What is streaming with Delta Tables and how does it work? 1️⃣8️⃣ Best practices for partitioning Delta Tables. 1️⃣9️⃣ How Delta Lake ensures data reliability and consistency? 2️⃣0️⃣ Real-world use cases where Delta Lake provides clear advantages. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐦𝐲 𝐌𝐞𝐝𝐢𝐮𝐦 𝐇𝐚𝐧𝐝𝐥𝐞 𝐭𝐨 𝐬𝐭𝐚𝐲 𝐮𝐩𝐝𝐚𝐭𝐞𝐝 - https://lnkd.in/dHhPyud2 𝗙𝗼𝗿 𝗠𝗲𝗻𝘁𝗼𝗿𝘀𝗵𝗶𝗽 - https://lnkd.in/gYn8Q39u 𝗙𝗼𝗿 𝗚𝘂𝗶𝗱𝗮𝗻𝗰𝗲 - https://lnkd.in/gfrPMQSj 𝗝𝗼𝗶𝗻 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 - https://lnkd.in/d3F93Y5u Riya Khandelwal

In Data Engineering, “How” Builds — But “Why” Defines. The other day, I was deep into an article about data modeling. It showed exactly how to do it — step-by-step, tool-by-tool. Halfway through, I caught myself thinking: 👉 Wait… but why this way? Why this tool? Why not another? That moment hit me. We, as Data Engineers, often chase the how — the newest framework, the next service, the cleverest architecture. But the real growth starts when we pair it with the why — the purpose, the trade-offs and the context. Because great data engineering isn’t about knowing every tool. It’s about knowing when, why, and how to use the right one — for the right reason. The how gets the job done. The why makes the job worth doing. Curious — which one drives your decisions more: the how or the why? #DataEngineering #DataMindset #Architecture #Learning #BigData #How #Why

Great blog post from an experienced data engineer on the non-glamorous but vital things one needs to do to deliver projects successfully: https://lnkd.in/eaNpGgtP

One of the most common questions asked in Data Engineering interviews is: 👉 “What are Delta Tables or Delta Lake?” If you’re preparing for roles in Databricks, Spark, or modern data platforms, chances are you’ll definitely face Delta-related questions. From my own experience, while appearing for interviews, I noticed that Delta Lake is a hot topic, and here are the Top 20 Delta Table questions I frequently came across: 1️⃣ What is Delta Lake and how is it different from a traditional data lake? 2️⃣ What are Delta Tables and why do we use them? 3️⃣ How does Delta Lake ensure ACID transactions? 4️⃣ Difference between Managed and External Delta Tables. 5️⃣ How Delta Lake handles schema evolution? 6️⃣ What is Time Travel in Delta Tables and how do you use it? 7️⃣ Explain Delta Log and its importance. 8️⃣ How Delta Lake supports concurrent reads/writes? 9️⃣ Difference between Delta Tables and Parquet. 🔟 What are CDC (Change Data Capture) capabilities in Delta Lake? 1️⃣1️⃣ How do you optimize Delta Tables for performance? 1️⃣2️⃣ What is the role of Z-Ordering in Delta Tables? 1️⃣3️⃣ How Delta Lake handles deletes and updates (MERGE operations)? 1️⃣4️⃣ Delta Lake file compaction and vacuuming – why is it needed? 1️⃣5️⃣ Difference between Delta Live Tables and Delta Tables. 1️⃣6️⃣ How Delta Tables integrate with Spark SQL? 1️⃣7️⃣ What is streaming with Delta Tables and how does it work? 1️⃣8️⃣ Best practices for partitioning Delta Tables. 1️⃣9️⃣ How Delta Lake ensures data reliability and consistency? 2️⃣0️⃣ Real-world use cases where Delta Lake provides clear advantages. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐦𝐲 𝐌𝐞𝐝𝐢𝐮𝐦 𝐇𝐚𝐧𝐝𝐥𝐞 𝐭𝐨 𝐬𝐭𝐚𝐲 𝐮𝐩𝐝𝐚𝐭𝐞𝐝 - https://lnkd.in/dHhPyud2 𝗙𝗼𝗿 𝗠𝗲𝗻𝘁𝗼𝗿𝘀𝗵𝗶𝗽 - https://lnkd.in/gYn8Q39u 𝗙𝗼𝗿 𝗚𝘂𝗶𝗱𝗮𝗻𝗰𝗲 - https://lnkd.in/gfrPMQSj Riya Khandelwal