An introduction to ETL and Azure Data Factory
ETL and Azure Data Factory both play a key role in extracting value from your data
Creating data integration pipelines
In Azure Data Factory, you define a pipeline that consists of activities representing the ETL steps. You can use pre-built connectors and activities to connect to your data source, perform transformations using built-in or custom code, and load the data into the target destination.
Mapping and transforming data
Azure Data Factory provides a graphical interface for defining data mappings and transformations. You can apply transformations such as filtering, sorting, aggregating, and joining data to transform it into the desired format. You might use Azure DataLake as storage for those dates since the raw data till the structured data.
Monitoring and managing pipelines
Once the pipeline is deployed, you can monitor its execution, track data movement, and troubleshoot any issues using Azure Data Factory’s monitoring and management features. A simple task list for configuring Azure Data Factory’s monitoring and management features.
1. Set up the Azure Data Factory:
Create an Azure Data Factory instance in the Azure portal:
Configure the required settings, such as the region, resource group, and integration runtime.
2. Set up the Azure DataLake:
Create an Azure DataLake in the Azure portal:
Configure different blobs for each kind of data or phase. Eg:
Blob container for raw data
Blob container for enriched data
Blob container for refined data
3. Define Linked Services:
It’s necessary to have a VPN or ExpressRoute to have communication between those different environments and then use the linked service.
Create a linked service to connect to the on-premises database, the source in this case. This involves providing the necessary connection details, such as server name, credentials, and database name.
Create a linked service for Azure Synapse Analytics, specifying the connection details for the target destination.
4. Create the datasets:
Define a dataset for the source data in the on-premises database. Specify the connection information and the structure of the data.
Create a dataset for Azure Synapse Analytics and Azure DataLake, defining the connection details and the target structure.
5. Build the pipeline:
Create a new pipeline in Azure Data Factory.
Add a copy activity to the pipeline. Configure the source dataset as the on-premises database and the destination dataset as Azure DataLake.
Specify the data mapping and transformation activities within the copy activity. This can include column mapping, data filtering, aggregations, or other transformations.
Configure any required data transformation activities, such as data cleansing, enrichment, or formatting.
6. Define dependencies and scheduling:
Define any dependencies between activities within the pipeline, ensuring that activities are executed in the correct sequence.
Set up a schedule for the pipeline to determine when it should run, whether it’s a one-time execution or a recurring schedule — e.g., daily, hourly, etc.
7. Monitor and execute the pipeline:
Deploy and validate the ETL pipeline.
Monitor the execution of the pipeline using Azure Data Factory’s monitoring capabilities.
Track the progress, identify any errors or issues, and troubleshoot as necessary.
8. Validate and verify the results:
Verify that the customer data has been successfully extracted from the on-premises database.
Confirm that the data has been transformed according to the defined mappings and transformations.
Validate that the transformed data has been loaded into Azure Synapse Analytics as expected.
By following these steps, you can build an ETL pipeline in Azure Data Factory to extract customer data from an on-premises database, apply necessary transformations, and load the transformed data into Azure Synapse Analytics for further analysis and reporting.
Advanced features and integration
Apart from the core ETL capabilities, Azure Data Factory integrates seamlessly with other Azure services, such as Azure Databricks, Azure Machine Learning, and Azure Logic Apps. This integration enables you to leverage advanced analytics, machine learning, and event-driven workflows within your ETL pipelines.