Question 1

Explain the difference between Azure Data Factory (ADF) and Databricks.

Accepted Answer

ADF is an orchestration and data-movement service; Databricks is a compute platform for analytics and ML. Why it matters: ADF excels at scheduling, branching, retries, and connectors—it's the 'conductor.' Databricks excels at heavy transforms (Spark), Delta Lake, and ML—it's the 'orchestra.' Scalability: ADF scales by parallelism (activities, self-hosted IR nodes); Databricks scales via cluster sizing and auto-scaling....

Question 2

Compare ADF vs. Databricks.

Accepted Answer

Why compare: Different roles—orchestration vs. compute. ADF: Low-code; 100+ connectors; scheduling; simple ELT. Databricks: Code-first Spark; heavy processing; ML. Architectural logic: ADF = plumbing—move data, schedule. Databricks = heavy lifting—transforms, ML. Often together: ADF triggers Databricks notebooks. Scalability: ADF is serverless; Databricks scales clusters. Cost: ADF = DIU + copy; Databricks = cluster hours. Use ADF for orchestration; Databricks for processing....

Question 3

Explain a linked service and how to create one.

Accepted Answer

Architectural logic: Linked service = connection definition (endpoint, auth) to external resource. No schema or path. Creation: Manage → Linked services → New → type (e.g., Azure Blob) → connection string or Key Vault reference. Auth: Account key, SAS, Service Principal, Managed Identity....

Question 4

Moving pipelines from development to production: ARM templates for deployment.

Accepted Answer

**Why IaC for pipelines**: Reproducibility, audit trail, environment parity. ARM (and Bicep) define ADF pipelines, datasets, linked services as JSON. **Architecture**: Use parameters for environment-specific values (dev/prod URLs, IR names). Reference Key Vault for secrets—never inline. In CI/CD: az deployment group create with parameter file. Use Git integration in ADF for version control; ARM deploys infra, Git syncs pipeline definitions....

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