Just about every analytics or data related project relies heavily on data engineering – or the ability to move and transform large amounts of data. This is not regarded as an especially exciting area and tends to be viewed as something that should simply work – preferably without much work. Unfortunately, the reality is quite different, and while data engineering has come a long way with automation during the last decade, the advent of the cloud has completely upended the old truths of how to build and architect a data engineering pipeline. It might seem like the obvious choice to apply the same established and well-performing on-prem architecture patterns to the cloud and call it a day. 

While this will most likely work, it will also guarantee a substantial cost increase as an on-prem pattern relies on abundant, cheap compute – the opposite of how the cloud works. Where cost savings on-prem are largely related to a discount when purchasing hardware, the cloud enables a fluid, pay-as-you-need-it way of using resources. This workshop will give you a comprehensive overview of the available data integration services in Azure, discuss the vast difference is architecture patterns and explore how you might reengineer your data engineering pipeline to get maximum benefit from the cloud model.

Stakeholders, architects, data engineers, data scientist, data analysts, security.

By including these personas or roles in a workshop on migrating analytics load to Azure, organizations can leverage their diverse expertise and perspectives to develop a comprehensive migration strategy, address potential challenges, and ensure a smooth transition to the Azure platform.

After successfully migrating your analytics load to Azure, the next step is to ensure your Power BI estate is optimized with our Power BI Estate Assessment workshop, providing a clear overview and insights into enhancing performance and maximizing ROI.