S u c c e s s   S t o r y

Cloud Datawarehouse to enable Analytics at scale


Migrate on-prem Data Warehouse to Cloud Data Warehouse for Advanced analytics at scale



Technology Used

  • Programming Languages: SQL
  • ETL Tools: Azure DataFactory
  • Datawarehouse: MS SQL Server, Synapse Analytics
  • Cloud: Azurer
  • CI/CD: Azure DevOps


The client is US-based and successfully doing beverage manufacturing. Client business covers the entire US and uses a traditional Data warehouse built on SQL Server. This on-premises data warehouse is not scaled so unable to large queries for reporting. Besides this, the business has to wait for a long to see the result of visualization on PowerBI in an interactive way.

The Client is looking for a scaled cloud warehouse where different teams can interact with data irrespective of data size and generates meaningful insight. To be a leader in the market Client is also interested in enabling machine learning and data science on its data. All these requirements can't be fulfilled by on-premises data warehouse so eventually cloud warehouse is the solution for these challenges.


Our Data wolves started by understanding the end goal of business and proposed Azure Synapse Analytics as a solution for clients' challenges. The proposed solution was carefully presented after evaluating POC built on other cloud platforms and selecting the cloud with minimum cost. Although the client's budget is not the limit, but meeting the client the expectation with the minimum upfront cost is at the core of WOlf of Data.

Microsoft Azure DataFactory is the primary tool for creating and scheduling ELT jobs. Synapse opted as a platform for building Cloud Warehouse as it is a fully managed service and easy to scale. Daily ELT jobs were scheduled to bring data from on-prem sources and stored it incrementally. Continuous Integration and delivery of data was made sure by Azure DevOps. The final solution fully meets the client's requirements and hence the client was fully satisfied.

Impact we created

  • - Bring scalability to the solution.
  • - A dashboard for monitoring data pipelines.
  • - Allow the different teams to play with data irrespective of data size.
  • - Make the system robust to handle large queries for PowerBI.
  • - Introduced notification mechanism for the status of data pipelines.
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