Azure has established itself as one of the leading cloud computing platforms, with a growing user base and a comprehensive suite of services and features for business intelligence. However, if you plan to move your BI platform to Azure there are a few things to consider.
In our previous blog on business intelligence, we shared our experience in implementing a new cloud reporting platform. Azure has established itself as one of the leading cloud computing platforms, with a growing user base and a comprehensive suite of services and features for business intelligence. However, having the tools is nothing without a solid migration strategy. This blog will explore the key considerations to ensure a smooth and successful migration to the Azure cloud.
Define your mission:
Before migrating to Azure cloud, defining your reporting platform requirements is essential. These requirements should cover your reporting platform's features, functionality and performance characteristics. You should also consider the data sources you will use and how you will integrate them into your reporting platform. For example, you should determine if you will incorporate historical data from your system. If your new reporting platform is being designed in the middle of a large ERP migration to the cloud, there are a few things to keep in mind.
Start from your as-is environment:
The first step is to evaluate your on-premises architecture and code. Reviewing the existing code and identifying any potential issues or performance bottlenecks that may arise during the migration process is vital. It is also essential to consider the code’s long-term maintenance and scalability. In many cases, it may be more effective to reuse the good parts of the code while rebuilding the features that have been poorly designed. However, this approach should be balanced against the need to minimize the risk of migration and the cost of rebuilding, which can be significant. Long story short, our advice would be to reinvent the wheel only if absolutely necessary, but definitely not to recycle legacy code that has been proven to be a pain and source of issues in the past.
Select your historical data:
Migrating historical data to a Data Lake on Azure can be a complex process. It is essential to plan the migration carefully to ensure that the data is accurate and accessible. Once you have identified the historical data that needs to be migrated, you must prepare the data for migration by cleansing and transforming it. Next, you can upload the data to the Data Lake using an appropriate method, such as Azure Data Factory or Azure Storage Explorer. Finally, it is necessary to validate the migrated data to ensure that it is accurate and accessible. If you aim to bring that data into your daily analysis, that’s also a good opportunity to challenge the business needs: do they really need everything? Indeed, even if a structure such as a Data Lake or a Synapse Dedicated Pool can support thousands of terabytes of data, you still need to wrap it up in your visualization tool, which could lead to performance issues – that’s also an opportunity to do some cleaning and keep only the relevant data.