With all the focus on cloud and Azure, have you ever wondered if you can run your traditional BI workloads within Azure? You may ask, should we be putting our traditional BI workloads that we’ve been using like the SQL Stack, SSIS, SSAS and relational databases in Azure; can we even do that?
The answer – yes; there are some new components and capabilities with Azure, and you can utilize those to migrate what you’re doing on-premise to Azure. The first way you can do this is to run them via SQL VMs within the Azure cloud. You can spin up SQL Server and everything you need there. As far as SSIS, analysis services and SQL Server, you can solve it just like you did on-prem, but run it within the cloud. Then you can take advantage of everything you get within an IaaS implementation.
The good news is now you can run all these components as Platform as a Service (PaaS) as well. So as SQL Server we can use Azure SQL Database. For workloads with larger-sized volumes, distributed queries or distributed big data within a SQL environment, Azure SQL Data Warehouse is a great solution.
Previously, we couldn’t run SSIS packages within the cloud, but with new capabilities with Azure Data Factory, we can now do this as a platform, without having to manage the server, cutting down on maintenance tasks and the daily chores of checking our server.
With SSAS, we have Azure Analysis Services, which works like a tabular instance of SSAS on-prem. We can employ tabular models there and run them in the cloud. So, we can orchestrate all these things together and take advantage of the PaaS capabilities that we get there. Once we're there, we gain the ability to orchestrate Data Lakes and put real time and advanced analytics on top of it.
It’s an exciting time to be in BI and IT. If you’ve been waiting for good Platform as a Service offerings to go to the cloud, the time is now. If you need help getting started or have more questions about running your traditional BI workloads in the cloud, click the link below or contact us – we’d love to help.