Azure Data Factory Architecture Explained Step by Step
Azure Data Factory is often described as a data integration service, but that description alone does not explain why it is so widely used in modern data platforms. At its core, Azure Data Factory is a workflow orchestration system designed for data movement and transformation across distributed environments. It does not replace databases, analytics engines, or processing frameworks. Instead, it connects them in a controlled, repeatable, and scalable way.
Many learners struggle with Azure Data Factory because they try to understand it feature by feature. Architecture thinking requires a different approach. You need to understand how responsibilities are separated, where execution happens, how data travels, and how control flows from start to finish.
This article explains Azure Data Factory architecture step by step, starting from conceptual foundations and moving toward real production-grade design thinking.
Step 1: Understand the Purpose of Azure Data Factory
Before architecture, clarity of purpose matters. Azure Data Factory exists to solve one fundamental problem: coordinating data workflows across multiple systems reliably and at scale.
It is not:
Instead, Azure Data Factory:
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Coordinates when data moves
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Controls how data is transformed
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Manages dependencies between steps
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Tracks execution and failures
Think of it as the control layer of a data platform.
Step 2: The High-Level Architectural View
At a high level, Azure Data Factory architecture can be visualized as four logical layers:
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Authoring Layer – where pipelines are designed
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Orchestration Layer – where execution logic is managed
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Execution Layer – where data movement and transformations actually run
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Monitoring Layer – where visibility and control are maintained
Each layer has a distinct responsibility. Mixing these responsibilities is the fastest way to build unstable pipelines.
Step 3: The Data Factory Itself (The Container Layer)
A Data Factory is the top-level container. It does not store data. It stores definitions.
Inside a Data Factory, you define:
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Pipelines
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Linked services
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Datasets
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Triggers
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Integration runtimes
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Parameters and variables
A critical architectural principle is this: Nothing inside a Data Factory should be environment-specific unless parameterized. Production-ready architecture treats the Data Factory as deployable infrastructure, not as a one-off configuration.
Step 4: Pipelines - The Backbone of Architecture
A pipeline represents a business workflow, not a technical task. Good pipelines answer questions like:
Bad pipelines are collections of random activities.
Architecturally strong pipelines:
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Have a clear start and end
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Separate ingestion, validation, transformation, and publishing stages
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Are reusable through parameters
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Can be re-run safely
A pipeline is not about copying data once. It is about defining repeatable behavior.
Step 5: Activities - Units of Work
Activities are the smallest execution units in Azure Data Factory. Architecturally, activities fall into three categories:
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Data movement activities
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Transformation dispatch activities
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Control activities
The most common mistake is assuming that activities perform heavy computation themselves. In reality, most activities delegate work to external systems. This delegation is intentional. It keeps Azure Data Factory lightweight and scalable.
Step 6: Linked Services - Connection Architecture
Linked services define how Azure Data Factory connects to external systems. Architecturally, linked services represent:
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Authentication method
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Network path
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Endpoint configuration
They do not define what data is used. They define how access is granted.
Strong architecture principles for linked services:
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One linked service per system per environment
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No embedded credentials in pipeline logic
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Centralized ownership and naming standards
Linked services are often where security failures occur, so they deserve special attention in architecture design.
Step 7: Datasets - Logical Data References
Datasets define what data is accessed, not how it is accessed. They sit between pipelines and linked services.
Architecturally, datasets:
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Abstract physical data locations
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Enable reuse across pipelines
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Allow schema and path consistency
Good datasets are parameterized. Bad datasets are hard-coded and copied repeatedly. A dataset should answer a simple question: “What shape of data lives here?”
Step 8: Integration Runtime – The Execution Engine
Integration Runtime is the most important and most misunderstood part of Azure Data Factory architecture. It defines:
Without Integration Runtime, pipelines are only instructions. Nothing moves.
Step 9: Types of Integration Runtime and Their Architectural Role
Azure Integration Runtime
This runtime is managed by Azure and is used when data sources and destinations are accessible from Azure.
Self-Hosted Integration Runtime
This runtime runs inside your private network.
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Architectural use cases: On-premises databases, private network resources, strict network isolation requirements.
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Architectural responsibility increases with this choice. You manage availability and performance.
Azure-SSIS Integration Runtime
This runtime exists to support SSIS package execution. Architecturally, it is a migration bridge rather than a modern design choice for new projects.
Step 10: How a Pipeline Actually Executes (Runtime Flow)
Understanding runtime flow prevents architectural confusion.
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A trigger starts the pipeline
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Parameters are evaluated
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Dependencies are resolved
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Activities are dispatched
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Integration Runtime executes movement or transformation
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Status and metrics are logged
Azure Data Factory controls the flow, not the computation.
Step 11: Data Movement Architecture
Data movement in Azure Data Factory follows these principles:
A stable architecture does not move data more than necessary.
Step 12: Transformation Architecture (ETL vs ELT)
Azure Data Factory supports both ETL and ELT patterns. Architectural decision factors include:
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Data volume
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Compute cost
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Governance requirements
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Latency expectations
ADF orchestrates transformations; it does not replace specialized engines.
Step 13: Security Architecture
Security is not an afterthought in Azure Data Factory architecture. Key architectural elements include:
Strong architecture assumes zero trust by default.
Step 14: Monitoring and Observability Architecture
Monitoring is not just failure detection. Architecturally, monitoring answers:
Production pipelines fail silently when observability is weak.
Step 15: CI/CD and Environment Architecture
Azure Data Factory is not designed for manual deployment. Mature architecture includes:
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Development environment
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Testing environment
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Production environment
Each environment shares structure but not configuration.
Step 16: Cost-Aware Architecture
Cost issues are architectural issues. Expensive pipelines are usually:
Efficient architecture is deliberate, not accidental.
Step 17: Common Architectural Mistakes to Avoid
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Treating ADF as a transformation engine
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Hard-coding paths and credentials
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Ignoring rerun scenarios
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Mixing orchestration and business logic
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Designing without monitoring
Avoiding these mistakes improves reliability more than adding features.
Step 18: A Simple Architectural Mental Model
If you remember only one model, remember this:
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ADF controls
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Other services compute
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Integration Runtime connects
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Pipelines define behavior
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Monitoring protects reliability
This model scales from small projects to enterprise platforms. Master these concepts in our Azure Data Engineering Online Training.
Frequently Asked Questions (FAQ)
1.What is Azure Data Factory architecture in simple terms?
Ans: It is a layered system that orchestrates data workflows while delegating execution to the right compute and network environments.
2.Is Azure Data Factory an ETL tool?
Ans: Azure Data Factory is primarily an orchestration tool that supports ETL and ELT patterns.
3.Why is Integration Runtime so important?
Ans: Because it determines where execution happens and how data crosses network boundaries.
4.Can Azure Data Factory work with on-premises systems?
Ans: Yes, using Self-Hosted Integration Runtime.
5.Does Azure Data Factory store data?
Ans: No. It stores workflow definitions, not actual data.
6.How does Azure Data Factory ensure scalability?
Ans: By separating orchestration from execution and using managed or delegated compute.
7.Is Azure Data Factory suitable for enterprise projects?
Ans: Yes, when designed with proper security, CI/CD, and monitoring architecture.
8.What is the biggest architectural mistake beginners make?
Ans: Assuming Azure Data Factory performs transformations itself instead of orchestrating them.
Final Summary
Azure Data Factory architecture is not complex, but it is precise. Each component exists for a reason. When you respect those boundaries, pipelines become reliable, scalable, and easy to manage. When you ignore them, even small workflows turn fragile.
Understanding architecture is what separates someone who can “build pipelines” from someone who can design data platforms. For a comprehensive understanding, explore our full curriculum in Azure Data Engineering Online Training.
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