The six building blocks of ADF, how data flows through them step by step, a real retail example, and how ADF compares with AWS Glue and Google Cloud Dataflow.
✍️ Azure Trainings trainers⏱ 4 min read📅 Updated 11 October 2026
ADF architecture is simple: a trigger starts a pipeline, the pipeline runs activities, activities use linked services and datasets to read and write data, and an integration runtime provides the compute. The result lands in a sink such as Data Lake, Azure SQL, Synapse or Fabric. This Azure Data Factory Architecture guide explains each part in simple steps.
The compute: Azure IR, self-hosted IR, Azure-SSIS IR. Keep this in mind when you plan for Azure Data Factory Architecture.
How it works
Step-by-step data flow in ADF
1Schedule / event
Trigger
2Order & logic
Pipeline
3Copy, transform
Activities
4Compute
Integration runtime
5Destination
Sink
↔️ Swipe the table sideways
ComponentRoleTriggerStarts the pipeline automatically or on demandPipelineHolds the activities and workflow logicActivitiesCopy, Data Flow, Web call, Stored Procedure and moreDataset / Linked serviceDescribe the data and how to connect to itIntegration runtimeThe compute that runs the activitiesSinkData Lake, Azure SQL, Blob Storage, Synapse and more
🧭 Want to draw and build this architecture yourself? Our trainers do it with you in live labs. Learn Azure Data Factory Architecture with live labs.
A retailer copies sales and stock data between systems by hand, so reports are slow and often out of date.
✅
The ADF solution
A scheduled trigger runs every morning → the pipeline reads sales from on-prem SQL Server through a self-hosted IR → a data flow cleans it → it lands in the data lake / Azure SQL → Power BI refreshes. Learners often ask us about this part of Azure Data Factory Architecture.
Typical result: reports ready much faster, fewer manual errors and fresher data for planning. (Illustrative example.)
Compare
ADF vs AWS Glue vs Google Cloud Dataflow
↔️ Swipe the table sideways
PointAzure Data FactoryAWS GlueGoogle Cloud DataflowMain purposeIntegration & orchestrationServerless ETLBatch & stream processingDesign styleVisual, low-code pipelinesVisual jobs or Spark codeCode with Apache BeamTransform engineData Flows (Spark) or external computeApache SparkApache Beam runnerHybrid / on-premSelf-hosted IRVia connectionsVia networkingBest forAzure and hybrid platformsAWS data lakesStreaming-heavy on Google Cloud
Why it works
Top benefits of ADF architecture
🧩
Modular
Add a source or step with one activity.
🛡️
Resilient
Built-in retries, timeouts and error paths.
📈
Scalable
Handles growing data volumes.
🔗
Hybrid
Connects on-prem and cloud securely.
♻️
Reusable
Parameters let one pipeline handle many tables.
👀
Easy to monitor
Run history and Azure Monitor alerts.
✅ Checkpoint: you can draw trigger → pipeline → activities → IR → sink from memory and explain each box.
Career
ADF jobs in India
ADF is a common skill in Azure data engineering job descriptions. Bengaluru and Hyderabad have the most openings, followed by Pune, Chennai, Mumbai and Delhi NCR. See Azure Data Factory jobs for roles, skills and salaries.
Learn ADF architecture hands-on Our runs at JNTU/KPHB, Hyderabad and live online, with labs on a live Azure subscription and DP-700-aligned content. Our course covers Azure Data Factory Architecture end to end.
You can learn Azure Data Factory Architecture in our live classroom batches near JNTU/KPHB or in live online batches with Azure Trainings. Book a free demo class to see how we teach before you join.
Yes. This Azure Data Factory Architecture guide is reviewed by our trainers and was last updated in October 2026.
🎓
Written by the Azure Trainings training team
Based on what we teach in our batches and see in real projects. Last updated 11 October 2026. We review this Azure Data Factory Architecture guide regularly. Spotted something outdated? Tell us on WhatsApp.
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