Databricks Interview Scenario | Join Two 500 GB Tables | Spark AQE, Data Skew & Photon Explained

Databricks & Apache Spark Real-Time Interview Scenarios | Scenario 4 How would you optimize a Databricks job that joins two massive tables, each containing 500 GB of data, when the current execution time is 90 minutes and the business expects it to finish within 25 minutes? In this video, we solve a real-world Data Engineering interview scenario using Apache Spark, PySpark, and Databricks. Topics Covered in This Video 1. Real-time Databricks interview scenario: joining two 500 GB tables 2. Why you should not blindly use a Broadcast Join 3. Implementing joins using PySpark DataFrames 4. Understanding Spark Physical Execution Plans 5. Spark UI analysis for performance troubleshooting 6. Shuffle Join and Shuffle Optimization 7. Data Skew detection and troubleshooting 8. Adaptive Query Execution (AQE) explained 9. AQE optimization: skew joins and shuffle partition coalescing 10. Photon Engine in Databricks 11. Photon vs Standard Spark Execution Engine 12. Catalyst Optimizer vs AQE vs Photon 13. Delta Lake optimization using OPTIMIZE and table statistics 14. Shuffle partition tuning and performance improvement 15. Production-level Spark performance troubleshooting Real-Time Business Scenario You are a Data Engineer at a large e-commerce company. You need to join two Delta tables: `fact_orders` — 500 GB `fact_payments` — 500 GB The tables are joined using `order_id` to generate a report containing customer IDs, order amounts, payment amounts, and payment statuses. The challenge is to reduce the job execution time from 90 minutes to 25 minutes without compromising data correctness. We will explore how to identify bottlenecks, reduce unnecessary data processing, investigate data skew, evaluate AQE, and optimize the execution plan. Who Should Watch This Video ✅ Aspiring Data Engineers ✅ Experienced Data Engineers preparing for interviews ✅ Databricks and PySpark developers ✅ Apache Spark developers ✅ Azure Databricks professionals ✅ Data Engineers preparing for production troubleshooting interviews ubscribe to NextGen Tech Era for more practical Data Engineering tutorials, real-time interview scenarios, Spark troubleshooting, Databricks projects, and production-level problem-solving. Our goal is to make complex Data Engineering concepts simple through practical examples and real-world scenarios. #Databricks #ApacheSpark #PySpark #DataEngineering #SparkPerformanceTuning