As data volumes continue growing, organizations need scalable ways to integrate data from diverse sources. This is where extract, transform, and load (ETL) shine. Azure etl process is a data integration process that:
- Extracts data from source systems
- Transforms data for analysis
- Loads data into a destination data store
Azure makes ETL simpler through Azure Data Factory, a cloud ETL service with an intuitive graphical interface. With over 10 years in data integration, I love Azure Data Factory for its flexibility and ease of use.
Extracting Data from Sources
The first step of ETL is extracting data from sources like databases or software applications. As a consultant, I often work with clients using on-premises and cloud data sources. Azure Data Factory can connect to over 90 data sources through native connectors.
For example, we integrated e-commerce transaction data from an on-premises Oracle database and Shopify’s cloud API into a client’s data warehouse. Azure Data Factory handled connecting and extracting data from these heterogeneous sources with just a few clicks!
| Data Source | Details |
| On-premises Oracle database | Contained 3 years of order history data |
| Shopify API | Cloud e-commerce platform with real-time order data |
Transforming Data for Analysis
After extraction, ETL solutions transform data for analysis. With Azure Data Factory, no coding is required to transform data using an intuitive visual interface.
Common transformations include:
- Filtering to relevant rows and columns
- Joining disparate sources
- Aggregating data like sums and counts
For the e-commerce client, we joined Oracle order history with Shopify’s real-time feed and aggregated sales by product category and month for the data warehouse. These transformations prepared the data for business intelligence dashboards.
Loading Data into Azure Data Stores
The final ETL stage loads processed data into a destination store, often a data warehouse or database. Azure Data Factory can load data into diverse Azure destinations like:
- Azure Synapse Analytics data warehouse
- Azure SQL Database relational database
- Azure Data Lake Storage for big data analytics
For the client, we loaded the aggregated sales data into Azure Synapse Analytics for fast SQL queries. Their business users gained self-service access to these curated datasets for analytics.
Orchestrating ETL Processes with Pipelines
Azure Data Factory ties together the extract, transform, and load steps through reusable data pipelines. These pipelines can be scheduled to automate the ETL process.
The Shopify and Oracle pipeline runs hourly to capture the latest e-commerce transactions. This keeps their Azure Synapse Analytics warehouse refreshed with hot data for reporting.
Through Azure Data Factory’s REST API, pipelines can also be triggered on-demand or from external events. Built-in monitoring provides alerts and logging for auditing.
Get Started with Azure ETL Today
I hope this overview gives you a sense of the end-to-end ETL process in Azure. With graphical pipelines, reusable connectors, and mapping tools, Azure Data Factory takes the complexity out of data integration.
To learn more, I recommend trying out Azure Data Factory and building a simple pipeline for your data. Microsoft also provides hands-on labs and tutorials.
If you have an upcoming data analytics project, I’m always happy to chat through the possibilities with Azure Data Factory. Reach out if you need any help orchestrating ETL or analytics processes!
