Databricks: From Zero To Hero: An interview guide

Zero to Hero in Databricks Batch Processing is an architect-level, end-to-end preparation guide for mastering Databricks Lakehouse batch architectures. It goes beyond theory to focus on real production scenarios, governance, performance, reliability, and interview-ready decision-making, making it ideal for senior data engineers and data architects preparing for Databricks-focused roles

Foundations

1. Lakehouse Architecture vs traditional Warehouse + Lake
2. Medallion Architecture (Bronze / Silver / Gold)
3. Batch vs Streaming trade-offs
4. Delta Lake internals (ACID, MVCC, Time Travel)

🔹 Governance & Security

1. Unity Catalog object model (Metastore → Catalog → Schema → Table)
2. Centralized vs Data Mesh governance
3. PII tagging, ABAC, masking policies
4. Audit logs, lineage, GDPR/CCPA compliance

🔹 Ingestion & Processing

1. Auto Loader, COPY INTO, CDC & Change Data Feed (CDF)
2. Idempotent batch pipelines
3. Late data handling, corrupt files, replay & backfill strategies

🔹 Orchestration & Data Quality

1. Delta Live Tables (DLT) vs Databricks Workflows
2. SLA/SLO enforcement
3. Control tables, manifests, monitoring & alerting

🔹 Performance & Cost Optimization

1. Spark UI diagnostics
2. File sizing, partitioning, Z-ORDER, Liquid Clustering
3. Photon engine
4. Cost governance using system tables

🔹 Reliability & Operations

1. Disaster Recovery (RPO/RTO)
2. Auditing and observability
3. Runbooks, playbooks, and DR drills

🔹 Advanced & Enterprise Topics

1. SCD Type 2 pipelines
2. CI/CD with Databricks Asset Bundles & Terraform
3. Hybrid & multi-cloud architectures
4. Optional ML, Feature Store & MLOps

🔹 Interview Readiness

1. Architect-level scenarios with structured answers
2. Trade-off discussions (cost vs latency vs reliability)
3. Capstone migration scenario
4. Real interview delivery frameworks