Automating ETL with Azure Data Factory
Smart Data implemented an Azure Data Factory solution to replace manual data load processes across a growing business. The new approach improved consistency, automated workflows, and freed up teams to focus on higher-value work.
Client
Confidential Enterprise Client
Industry
Data Management
Timeline
2021
Project Overview
The client maintained several internal applications to handle data loading, each built and owned by individual teams. While technically functional, this approach required extra development effort for every update, leading to inconsistent performance and limited scalability.
Data was coming from multiple sources including Excel files, on-premise SQL Server, Azure SQL, and Oracle. With growing business needs and increasing data transfer volumes, the client needed a more stable and scalable ETL solution. Smart Data was brought in to automate the process, beginning with a pilot project to ingest Excel data into Azure SQL and notify stakeholders of successful or failed loads.
Project Challenge
The existing process was overly manual and siloed across teams. Specific challenges included:
Each team maintained its own custom data loading apps
Enhancements required full development cycles and QA
Multiple data sources added integration complexity
Lack of automation led to inconsistent reporting and delays
No centralized email notifications or audit trail for load activity
The client needed a cost-effective, low-maintenance solution that could grow with their needs and eliminate these inefficiencies.
Project Approach
Smart Data recommended a cloud-native solution built entirely within the Azure ecosystem. The technical implementation included:
Azure Storage Container
Field engineers uploaded Excel files directly to a secured storage container with proper access controls.
Azure SQL Database
Smart Data created a dedicated SQL login for Azure Data Factory to write data into the client’s destination tables.
Azure Key Vault
All database credentials and access keys were stored securely in Azure Key Vault and retrieved dynamically.
Azure Data Factory
Data pipelines were built using ADF to handle:
File validation
Data deduplication and transformation
Error capture and reporting
Insertion of clean data into SQL tables
Creation of detailed logs for skipped or invalid records
Azure Logic Apps
Because ADF does not send emails natively, Smart Data connected it to Logic Apps to send automated success or failure notifications with detailed logs.
Azure DevOps
All infrastructure and code were managed via Azure DevOps. Pipelines were created for automated deployment across development, QA, and production environments.
Project Results
The client now uses Azure Data Factory as a core component of its enterprise data architecture. The solution has reduced manual workload and provided a standardized path forward for other ETL initiatives.
Key Technologies
Key technologies included Azure Data Factory, Azure SQL, Azure Storage Containers, Azure Key Vault, Azure Logic Apps, and Azure DevOps for pipeline management and deployment automation.
