Case Studies Details

Data Transformation Using Azure Data Factory and SQL Server

Overview

A mid sized retail company was looking to modernize its data infrastructure. The client had multiple legacy systems with siloed data, which made it difficult to generate meaningful insights. The company needed a streamlined data transformation and warehousing solution to improve reporting and analytics for better decision making and enable scalability.

Challenges

  • Data Silos -   Distributed data across multiple sources prevented company to have unified view of their operations.
  • Data Quality Issues -   Existing data was often incomplete, inconsistent or outdated.
  • Manual Processes -   Batch processing and outdated data hindering real time insights and timely decisions.

Solution Approach

Initial Data Assessment

Conducted a thorough assessment of client’s existing data sources, data quality, and reporting needs.

ETL Pipeline Development
  • Built ETL Pipelines in Azure Data Factory for automated data ingestion, transformation and loading in DW
  • Used ADF’s built-in connectors for seamless integration with various data sources
Data Warehouse Design

Designed the warehouse schema in SQL Server, optimizing it for analytical queries.

  • Published By:

    #taskone Consulting Group

  • UAE

    Meydan Grandstand 6th Floor Maydan Road Nad AI Sheba Dubai U.A.E.

  • Industry

    Business

  • Founders

    Sumit Shukla, Swapnil Srivastava

  • Case Study

    Data Transformation Using Azure Data Factory and SQL Server

  • Date

    11 Mar 2025

  • View Author's Profile

Result

Improved Data Quality

The automated ETL process reduced manual errors and ensured that data was consistent across.

Faster Insights

Real time data processing enabled the client to access up to the minute analytics for informed decision making.

Cost Efficiency

By leveraging cloud based services like ADF and SQL Server on Azure, the client was able to reduce Infra cost and improve operational efficiency.

Conclusion

Implementing a data warehousing solution with Azure Data Factory and SQL Server helped the client address data challenges, enhancing data quality, speed and scalability. The solution met their current needs and set them up for future growth and adaptability.

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