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Datawarehouse technology emerged about 27 plus years ago. Organizations were looking for an improved and effective way to analyse their data, primarily to better serve their customers & in the process discover newer business opportunities.
With these objectives in mind Enterprise Data Warehouses (EDW) were designed and deployed. As the usage increased, several users started demanding more data for reporting and analysis purposes. The on-premise EDWs started facing storage constraints due to exponential growth of stored data.
Thus, emerged a need to modernize the EDW. Datawarehouse architecture started undergoing changes. These changes were necessitated by two main reasons,
In one of our earlier blogs, we had mentioned about organizations having sizable investments in existing on-premise EDW infrastructure and need to complement/ augment such existing EDW with a data lake to address processing needs of unstructured data.
When we talk about “Augmenting/ Modernizing EDW,” we are referring to reengineering and refactoring current EDW architecture to address some of the key business problems & demands. We also refer to an opportunity for creating value from existing and modernized EDW infrastructure.
Some of the key drivers for such EDW modernization would arise out of:
Cloud computing enables businesses to use virtual resources offering flexibility and scalability when it comes to EDW, reporting and analytics needs. Thus, Cloud Datawarehouse becomes a feasible alternative to on-premise Datawarehouse.
There are two options your organizations can explore:
Pros and cons for both options would need to be deliberated. Few of the organizations we are interacting with are undertaking due diligence exercise to assess current state, determine changes needed, seek recommendations to choose best technology option and plan phased implementation.
Category: Data Analytics, RPA & AI