Databricks Interview Guide with Gopichand Godichala

Preparing for a Databricks or Azure Data Engineering interview? This is the only resource you need.

I've compiled 12+ years of data engineering experience — including real interview questions I've faced and asked — into one comprehensive guide.

🔷 WHAT'S INSIDE:

📘 Section 1: Databricks Platform & Lakehouse Architecture

→ 15 Q&As + 5 architecture design scenarios

→ Full platform comparison: Databricks vs Open-Source Spark

→ Lakehouse explained with visual diagrams

→ Medallion Architecture with production PySpark code (Bronze → Silver → Gold)

→ Cluster types, Serverless compute, Asset Bundles

→ SCENARIO: Design a data platform for 50M daily transactions

→ SCENARIO: Optimize a cluster costing ₹5L/month

→ SCENARIO: Implement CI/CD for Databricks

📗 Section 2: Delta Lake Deep Dive

→ 20 Q&As + 5 scenarios

→ Transaction log (_delta_log) — how ACID actually works, explained visually

→ OPTIMIZE, Z-ORDER, VACUUM with before/after metrics

→ Liquid Clustering vs Z-Ordering (2026 trending topic)

→ MERGE INTO with full SCD Type 2 implementation code

→ Change Data Feed (CDF), UniForm, Deletion Vectors

→ Schema enforcement vs evolution — when to use each

→ SCENARIO: Recover from accidental DELETE using time travel

→ SCENARIO: Handle schema drift from upstream sources

📕 Section 3: Unity Catalog & Data Governance

→ 12 Q&As + 4 scenarios

→ Three-level namespace explained with diagrams

→ Row-Level Security — full implementation with SQL code

→ Column Masking — hide PII (SSN, account numbers)

→ Data lineage, System Tables, Attribute-Based Access Control (ABAC)

→ SCENARIO: Implement governance for 50 teams and 200+ workspaces

📙 Section 4: Spark Performance & Optimization

→ 15 Q&As + 5 scenarios

→ Step-by-step Spark UI diagnosis flowchart (visual)

→ 7 common performance mistakes with fixes

→ AQE, Photon Engine, broadcast vs sort-merge joins

→ Data skew — detection and 3 different fix strategies

→ Shuffle spill, partition tuning, Python UDF alternatives

→ SCENARIO: Reduce a 6-hour job to under 1 hour

→ SCENARIO: Join a 1TB fact table with a 10MB dimension — why it's slow

📘 Section 5: Streaming & Auto Loader

→ 10 Q&As + 4 scenarios

→ Auto Loader — complete implementation with schema evolution

→ Trigger modes — when to use availableNow vs processingTime

→ Watermarks — why they're critical and what breaks without them

→ Checkpoints, foreachBatch, stream-stream joins

→ SCENARIO: Handle late-arriving data in production

📗 Section 6: Workflows, DLT & Orchestration

→ 10 Q&As + 4 scenarios

→ DLT expectations (@dlt.expect, expect_or_drop, expect_or_fail)

→ Databricks Workflows vs Azure Data Factory — decision framework

→ CI/CD with Asset Bundles + GitHub Actions

→ Repair-and-rerun, task values, shared clusters

📕 Section 7: GenAI, RAG & ML on Databricks (2026 Trending)

→ 10 Q&As + 3 scenarios

→ Mosaic AI suite — Model Serving, AI Gateway, Vector Search

→ Complete RAG pipeline design with architecture diagram and code

→ MLflow, Feature Engineering, fine-tuning foundation models

→ SCENARIO: Build a document Q&A system for a bank

📙 Section 8: End-to-End Architecture Design (Mega Scenarios)

→ 5 comprehensive architect-level scenarios

→ Real-time fraud detection system design

→ Legacy SQL Server to Lakehouse migration plan

→ Customer 360 platform with identity resolution

→ Each scenario includes architecture diagram + technology choices + code

🔥 BONUS: PySpark Coding Interview Questions

→ 10 hands-on coding problems with solutions

→ Deduplication, incremental loads, nested JSON flattening

→ Window functions, pivoting, SCD Type 1 implementation

→ Data quality quarantine pattern

→ Reusable MERGE function with audit columns

⚡ BONUS: 20 Rapid-Fire Quick Q&As

→ One-liner answers for the speed round

→ Covers shuffle partitions, execution order, transformations, schema basics

📋 BONUS: Comparison Cheat Sheets

→ Delta Lake vs Iceberg vs Hudi — when to use which

→ Databricks compute options — cost vs performance matrix

→ Orchestration decision guide — Workflows vs ADF vs Airflow

📥 BONUS: Downloadable Databricks Notebook

→ Production-ready Medallion Architecture notebook

→ Import directly into your Databricks workspace

→ Complete Bronze → Silver → Gold pipeline with sample data

🔷 BY THE NUMBERS:

✅ 100+ interview questions with detailed answers

✅ 30+ real-world scenarios with architecture diagrams

✅ 10 PySpark coding problems with production-quality solutions

✅ 20 rapid-fire Q&As for speed rounds

✅ 15+ colorful architecture & flow diagrams

✅ Comparison cheat sheets for quick revision

✅ Downloadable Databricks notebook

✅ Printer-friendly HTML format (white background, colorful accents)

🔷 WHO IS THIS FOR:

→ Data engineers preparing for Databricks / Azure interviews

→ Mid to senior engineers targeting Lead / Architect roles

→ Anyone appearing for Databricks Certified Data Engineer exam

→ Engineers switching from traditional DW to Lakehouse

🔷 ABOUT THE AUTHOR:

Gopichand Godichala — Lead Data Engineer with 12+ years of experience in data engineering, from Oracle/Informatica to Azure Databricks and GenAI. Databricks Certified Data Engineer Professional. Azure DP-203 Certified.

YouTube: Insight_Into_Data — weekly Databricks deep dives with live demos.

💰 Launch Price: ₹59 (Regular: ₹149) — Limited time offer for early supporters.