Question 1

Difference between pipelines and data flows in ADF

Accepted Answer

Architectural logic: Pipelines = orchestration (activities, flow); Data Flows = Spark-based transformation activity. Pipelines orchestrate; Data Flows transform. A pipeline can contain Copy, Lookup, Data Flow, etc. When to use: Copy for simple move; Data Flow for filter, join, aggregate. Scalability: Data Flow runs on Spark; tune partitions. Cost: Data Flow consumes more than Copy....

Question 2

Fabric dataflows vs. ADF dataflows

Accepted Answer

Architectural difference: Fabric Dataflows = Power Query–style, Fabric-native, OneLake. ADF Dataflows = Spark-based, ADF-native, Azure-focused. When: Fabric for Fabric-centric, Power Query users. ADF when already in ADF, need Azure integration....

Question 3

Fabric pipelines vs. ADF pipelines

Accepted Answer

Architectural difference: Fabric Pipelines = Fabric-native, OneLake, built on ADF. ADF Pipelines = standalone, hybrid, multi-cloud. When: Fabric for Fabric workloads. ADF when Fabric not primary or need ADF connectors....

Question 4

Running multiple notebooks - dbutils.notebook.run()

Accepted Answer

**Why dbutils.notebook.run()**: Modular workflows—reusable notebook components, parameterized execution. Syntax: dbutils.notebook.run("/path/to/notebook", timeout_seconds, {"param": "value"}). Returns the last evaluated expression. **Difference from %run**: %run executes inline in the same context; variables are shared. dbutils.run runs in a separate Spark context; no variable sharing; returns output....

Question 5

Types of Integration Runtimes (IR) - self-hosted, Azure, SSIS

Accepted Answer

**Why IR type matters**: Data lives in different places; IR is the compute that moves and transforms it. **Azure IR**: Managed, serverless. For cloud-to-cloud (Azure SQL, Blob, ADLS). Auto-scales; zero maintenance. Use by default. **Self-Hosted IR**: Runs on-prem or in your VNet. For accessing on-prem SQL, file shares, or private endpoints. You manage VM, updates, scaling. **Azure-SSIS IR**: Hosts SQL Server Integration Services. For lifting existing SSIS packages....

Question 6

Unity Catalog - role in managing and securing data

Accepted Answer

**Why Unity Catalog**: Centralized governance—access control, lineage, audit, discovery. Replaces table-level ACLs with a consistent model across workspaces. **Architecture**: Three-level hierarchy: Metastore → Catalog → Schema → Table. Metastore is account-level; catalogs group schemas; schemas contain tables. RBAC via grants (SELECT, MODIFY, etc.) on tables, views, schemas. **Lineage**: Tracks data flow from source to consumption; critical for impact analysis and compliance....

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