| Troubleshooting | L37-L138 | Diagnosing and fixing Databricks errors, job and compute failures, connector/ingestion issues, SQL error codes, and performance/debugging problems across Spark, AI, Lakeflow, and tooling. |
| Best Practices | L139-L313 | End-to-end Databricks best practices for performance, cost, governance, streaming, ML/LLM/RAG, BI, Lakeflow, Vector Search, and operational reliability across Azure Databricks workloads. |
| Decision Making | L314-L401 | Guides for choosing Azure Databricks architectures, compute, runtimes, ML/LLM options, and detailed migration paths (Unity Catalog, Delta, SQL, Connect, MLflow, serverless, Lakebase, and networking). |
| Architecture & Design Patterns | L402-L444 | Architectural blueprints and patterns for Databricks: lakehouse, networking, storage, HA/DR, governance, performance, ML/MLOps, RAG/agents, Lakebase, streaming, and external data access. |
| Limits & Quotas | limits-quotas.md | Limits, quotas, and constraints for Databricks compute, AI/BI, connectors, Lakeflow, Lakebase, model serving, tokens, data types, and Unity Catalog resources, plus related workarounds. |
| Security | security.md | Identity, access control, encryption, networking, compliance, and secure integrations for Azure Databricks, Unity Catalog, Lakeflow, Lakebase, apps, and external data sources. |
| Configuration | configuration.md | Configuring and administering Azure Databricks: accounts, workspaces, security, networking, compute, storage, jobs, ML/serving, Lakehouse/Unity Catalog, Lakeflow, apps, and system-table–based monitoring. |
| Integrations & Coding Patterns | integrations.md | Patterns and APIs for integrating Databricks with external data systems, tools, and AI/ML frameworks, plus detailed PySpark/SQL function references and Lakehouse Federation/streaming examples. |
| Deployment | deployment.md | Deploying and operating Databricks apps, agents, models, jobs, and infrastructure using CI/CD, IaC, bundles, serving, Terraform, Git, and region/release planning. |
