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HomeDesigning Microsoft Azure Infrastructure Solutions (AZ-305)Quick Note — Recommend a Solution for Data Integration
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Quick Note — Recommend a Solution for Data Integration

AZ-305 › Unit 2 › Design data integration › Recommend a solution for data integration

Quick Note — Recommend a Solution for Data Integration

Data integration moves, transforms, coordinates, and publishes data across systems. The architect's challenge is rarely “Which Azure service can copy a file?” Many services can. The useful question is: Which integration model satisfies the workload's latency, transformation, connectivity, governance, and operating constraints without turning every data movement into custom code?

Retrieval target

Objective
Recommend a Solution for Data Integration
Mode
Closed-book recall
Target time
5 minutes
Escalation
Open the full lesson after a miss

Decision anchors

PromptCompact answer
Azure Data Factory (ADF)A serverless, fully managed data-integration service for ETL / ELT. ADF doesn't store data — it orchestrates ingestion from sources, optional transformation, and publishing to sinks. Pipelines can be built via Portal, Bicep / ARM templates, .NET / Python SDK, REST, or PowerShell. Pay-per-pipeline-run + activity-runtime; no idle cost.
Linked Service vs Dataset (ADF)Linked Service — the connection-string equivalent: tells ADF how to reach an external resource (server name, credentials, region). One linked service per data store / compute. Dataset — a typed view over data accessible through a linked service: pointer to a specific blob path + format, a specific table, a specific queue. Many datasets share one linked service.
Pipeline, Activity, Trigger (ADF)Pipeline — a logical grouping of activities that runs as a unit. Activity — one step (copy, transform, control flow). Three kinds: data movement (Copy), transformation (Mapping Data Flow, Databricks notebook, Stored Proc), control (ForEach, If, Wait, ExecutePipeline). Trigger — schedules a pipeline run: schedule, tumbling window, or event-based.
Integration Runtime (IR)The compute that runs ADF's activities. Three types: Azure IR (Microsoft-managed, serverless, public network); Self-hosted IR (Windows VM you deploy on-prem or in a VNet — makes outbound HTTPS only, the bridge to private data stores); Azure-SSIS IR (managed cluster that natively executes lift-and-shifted SSIS packages).

Read the answers once, then cover the right-hand column and reconstruct each one from the prompt. A useful answer names the requirement, the recommended control or service boundary, and the nearest alternative it rejects. If you can only recognize the answer after seeing it, retrieval is not yet secure.

Turn recall into an architecture answer

For recommend a solution for data integration, state: choose X because constraints A and B apply; reject Y because it fails C; validate with evidence D.

Ninety-second explanation

Without notes, explain:

  1. What requirement signals this learning objective rather than a neighbouring one?
  2. Which two solution families are most likely to be compared?
  3. Which hard constraint eliminates the strongest distractor?
  4. What identity, network, data, or failure boundary must appear in the design?
  5. Which operational test would prove the recommendation works?

Then compare your explanation with the full lesson. Record the missing decision rule—not merely the missed product name—in your error log.

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When to open the full lesson

Open the curriculum-linked lesson when you cannot explain a comparison, when a scenario depends on a numeric limit or SKU feature, or when the service is on a retirement path. Use current Microsoft Learn documentation for availability, limits, pricing, naming, and migration milestones; the quick note is intentionally compact.

Source and freshness

Derived from the linked AZ-305 lesson and retrieval deck, grounded in both attached course sources. Reviewed 2026-08-02. Current Microsoft documentation controls changing product contracts.

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Recommend a Solution for Data Integration — quick retrieval

Card 1 of 4

Front of flashcard 1 of 4

Azure Data Factory (ADF)

easy

A serverless, fully managed data-integration service for ETL / ELT. ADF doesn't store data — it orchestrates ingestion from sources, optional transformation, and publishing to sinks. Pipelines can be built via Portal, Bicep / ARM templates, .NET / Python SDK, REST, or PowerShell. Pay-per-pipeline-run + activity-runtime; no idle cost.

data-factoryetlelt

Recommend a Solution for Data Integration — quick retrieval

Card 1

Front

Azure Data Factory (ADF)

Back

A serverless, fully managed data-integration service for ETL / ELT. ADF doesn't store data — it orchestrates ingestion from sources, optional transformation, and publishing to sinks. Pipelines can be built via Portal, Bicep / ARM templates, .NET / Python SDK, REST, or PowerShell. Pay-per-pipeline-run + activity-runtime; no idle cost.

Card 2

Front

Linked Service vs Dataset (ADF)

Back

Linked Service — the connection-string equivalent: tells ADF how to reach an external resource (server name, credentials, region). One linked service per data store / compute. Dataset — a typed view over data accessible through a linked service: pointer to a specific blob path + format, a specific table, a specific queue. Many datasets share one linked service.

Card 3

Front

Pipeline, Activity, Trigger (ADF)

Back

Pipeline — a logical grouping of activities that runs as a unit. Activity — one step (copy, transform, control flow). Three kinds: data movement (Copy), transformation (Mapping Data Flow, Databricks notebook, Stored Proc), control (ForEach, If, Wait, ExecutePipeline). Trigger — schedules a pipeline run: schedule, tumbling window, or event-based.

Card 4

Front

Integration Runtime (IR)

Back

The compute that runs ADF's activities. Three types: Azure IR (Microsoft-managed, serverless, public network); Self-hosted IR (Windows VM you deploy on-prem or in a VNet — makes outbound HTTPS only, the bridge to private data stores); Azure-SSIS IR (managed cluster that natively executes lift-and-shifted SSIS packages).

Recommend a Solution for Data Integration — quick retrieval

Card 1

Front

Azure Data Factory (ADF)

Back

A serverless, fully managed data-integration service for ETL / ELT. ADF doesn't store data — it orchestrates ingestion from sources, optional transformation, and publishing to sinks. Pipelines can be built via Portal, Bicep / ARM templates, .NET / Python SDK, REST, or PowerShell. Pay-per-pipeline-run + activity-runtime; no idle cost.

Card 2

Front

Linked Service vs Dataset (ADF)

Back

Linked Service — the connection-string equivalent: tells ADF how to reach an external resource (server name, credentials, region). One linked service per data store / compute. Dataset — a typed view over data accessible through a linked service: pointer to a specific blob path + format, a specific table, a specific queue. Many datasets share one linked service.

Card 3

Front

Pipeline, Activity, Trigger (ADF)

Back

Pipeline — a logical grouping of activities that runs as a unit. Activity — one step (copy, transform, control flow). Three kinds: data movement (Copy), transformation (Mapping Data Flow, Databricks notebook, Stored Proc), control (ForEach, If, Wait, ExecutePipeline). Trigger — schedules a pipeline run: schedule, tumbling window, or event-based.

Card 4

Front

Integration Runtime (IR)

Back

The compute that runs ADF's activities. Three types: Azure IR (Microsoft-managed, serverless, public network); Self-hosted IR (Windows VM you deploy on-prem or in a VNet — makes outbound HTTPS only, the bridge to private data stores); Azure-SSIS IR (managed cluster that natively executes lift-and-shifted SSIS packages).