Source legacy database
Cloud / Knowledge library
Azure Data Factory
Orchestrates data movement and transformation through managed pipelines and connectors
Why learn Azure Data Factory?
Teams use Azure Data Factory to adopt data integration and ETL without building the complete underlying platform themselves.
The problem it solves
Builds and operates pipelines that move and transform data across systems
Where it came from
Azure Data Factory is Microsoft Azure's offering for data integration and ETL. Its closest catalog counterparts are AWS Glue and Google Cloud Data Fusion; they address the same broad need but are not feature-for-feature identical.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain the main responsibility of Azure Data Factory.
- Choose a workload that fits Azure Data Factory.
- Identify one operational tradeoff of Azure Data Factory.
- Compare Azure Data Factory with AWS Glue.
- Compare Azure Data Factory with Google Cloud Data Fusion.
Documentation and sources
- Microsoft Azure product catalog (azure.microsoft.com)
Connect the ideas
Alternatives
Real World Example
Retail Inventory Synchronization Pipeline
Retailers managing multi-site inventory systems.
The business problem
Manual data reconciliation between on-premises legacy point-of-sale databases and cloud-based inventory management systems causes significant latency and data inconsistencies.
How this topic solves it
Azure Data Factory orchestrates data movement and transformation through managed pipelines and connectors, automating the extraction of on-premises sales data and loading it into the cloud data lake for centralized processing.
- Configure a self-hosted integration runtime to securely connect to the on-premises legacy database.
- Define a linked service to establish the connection string and authentication credentials for the source database.
- Create a copy activity within a pipeline to move raw sales data from the source to the cloud storage.
- Implement a mapping data flow to transform and clean the data before it reaches the final destination.
Shows the flow of data from on-premises systems to the cloud via ADF.
Bridge for hybrid connectivity
Orchestration engine
Destination storage
- On-Premises POS → Extracts → Self-Hosted IR
- Self-Hosted IR → Transfers → ADF Pipeline
- ADF Pipeline → Loads → Cloud Data Lake
Expected business outcome
Automated, reliable, and timely synchronization of inventory data across all retail locations without manual intervention.
Limitations and checks
The self-hosted integration runtime requires a dedicated machine on the local network, which introduces a dependency on local infrastructure uptime and maintenance.