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Automated data pipelines replacing manual ETL processes

Smart Data replaced manual data load processes with an automated Azure Data Factory solution. The new ETL architecture improved data accuracy, reduced maintenance and enabled stakeholder notifications across multiple data sources.

Built onAzureAzure Data FactorySQL Server

Industry
Data Management
Engagement
Data Platform
A data team working at their desks in a bright office

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.

The 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.

A long aisle between rows of server racks in a data center

The solution

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.

The results

  • Automated ingestion of Excel files with validation and error handling

  • Scalable architecture that supports growing data volumes

  • Stakeholder notifications triggered automatically on every run

  • Eliminated need for individual teams to maintain custom loaders

  • Centralized logic and configuration to reduce maintenance effort

  • Full approval by client’s architecture team for future expansion

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.

Client

Industry

Technology

Platform and technology

  • SQL Server
  • Azure
  • Azure Data Factory
  • Azure DevOps
  • ETL & Data Pipelines

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  2. An engineer looks at what’s running

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  3. You get a written proposal

    Covering scope, team, approach and price.

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