Azure Databricks Overview
Who this is for:
Architecture / Concept Overview: Azure Databricks Overview
%%{init: {"theme":"base","themeVariables":{"background":"#0B0E14","primaryTextColor":"#E0E6ED","lineColor":"#5D6470","darkMode":true,"primaryColor":"#2E4A4A","secondaryColor":"#374151","secondaryTextColor":"#E0E6ED","tertiaryColor":"#111827","tertiaryTextColor":"#E0E6ED","edgeLabelBackground":"#1f2937"}}}%%
flowchart LR
classDef source fill:#3F4B59,stroke:#9CA3AF,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef ingestion fill:#5A4B36,stroke:#C9A86B,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef processing fill:#535072,stroke:#8E82B4,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef storage fill:#2E4A4A,stroke:#5FAFA8,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef serving fill:#3D5550,stroke:#6BB7AA,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef governance fill:#5A3F52,stroke:#C28BB0,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
AAD[Azure AD Identity] -->|Auth| WS[Databricks Workspace]
ADF[Azure Data Factory] -->|Orchestrate| WS
EH[Event Hubs] -->|Stream| WS
WS -->|Read/Write| ADLS[ADLS Gen2]
WS -->|Query| SQL[Databricks SQL Warehouse]
WS -->|Train| ML[MLflow on Databricks]
SQL -->|Visualize| PBI[Power BI]
AAD:::governance
ADF:::ingestion
EH:::source
WS:::processing
ADLS:::storage
SQL:::serving
ML:::processing
PBI:::serving
*Azure Databricks workspace interactions showing identity, orchestration, storage, and serving integrations.*
%%{init: {"theme":"base","themeVariables":{"background":"#0B0E14","primaryTextColor":"#E0E6ED","lineColor":"#5D6470","darkMode":true,"primaryColor":"#2E4A4A","secondaryColor":"#374151","secondaryTextColor":"#E0E6ED","tertiaryColor":"#111827","tertiaryTextColor":"#E0E6ED","edgeLabelBackground":"#1f2937"}}}%%
graph TD
classDef source fill:#3F4B59,stroke:#9CA3AF,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef ingestion fill:#5A4B36,stroke:#C9A86B,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef processing fill:#535072,stroke:#8E82B4,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef storage fill:#2E4A4A,stroke:#5FAFA8,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef serving fill:#3D5550,stroke:#6BB7AA,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef governance fill:#5A3F52,stroke:#C28BB0,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
PLAT[Azure Databricks Platform] --> DE[Data Engineering]
PLAT --> DS[Data Science & ML]
PLAT --> DA[Data Analytics]
DE --> ETL[ETL Pipelines]
DE --> DLT[Delta Live Tables]
DS --> NB[Notebooks - Python/R/Scala]
DS --> MLF[MLflow Tracking & Registry]
DA --> SQLW[SQL Warehouses]
DA --> DASH[Dashboards & Alerts]
PLAT:::processing
DE:::ingestion
DS:::processing
DA:::serving
ETL:::ingestion
DLT:::ingestion
NB:::processing
MLF:::processing
SQLW:::serving
DASH:::serving
*Azure Databricks platform capabilities spanning data engineering, data science, and analytics workloads.*
Key Terms
Prerequisites and Setup
- An Azure subscription (free trial works for evaluation)
- Azure portal access or Azure CLI with appropriate permissions
- Basic familiarity with Apache Spark concepts (RDDs, DataFrames, transformations)
- Understanding of Azure resource management (resource groups, RBAC)
Step-by-Step Implementation
Configuration Reference
| Parameter | Description | Default | Recommended |
|---|---|---|---|
| SKU | Workspace tier (standard, premium, trial) | standard | premium for production |
| Spark Version | Databricks Runtime version | latest LTS | LTS for stability |
| Node Type | VM size for cluster workers | varies | DS3_v2 for general, NC-series for GPU |
| Autoscale | Dynamic worker scaling | disabled | Enable with min/max bounds |
| Auto-termination | Minutes of inactivity before shutdown | 120 | 30 for dev, 60 for production |
| Spot Instances | Use Azure Spot VMs for workers | disabled | Enable for fault-tolerant batch |