Industry Specific Data Models & Workflows – Many on-premise EDWs make
use of vendor specific data models and workflows targeted to specific verticals.
These allow businesses to quickly adopt an EDW that matches their needs, and
customize it for their unique situations. This can present challenges in the
cloud because of the different underlying technologies. Use cases should be
evaluated to determine if the logic and data models can easily move to a cloud
based EDW, or will require a level of redevelopment prior to implementation.
ETL Vendor Support for Cloud Integration – Many ETL technologies in use
today were built before the growth of cloud capabilities. Therefore, many ETL
vendors are now playing catch-up to add capabilities for natively accessing
cloud EDW and other relational stores. Any ETL tools that will be leveraged
should be evaluated to determine if they will support the cloud based technologies targeted for use and the level of support the vendor will provide. While
making the change, it might be advantageous for the organization to also evaluate native cloud data integration tools.
Proprietary Vendor Development and Analytical Languages – Many EDW
platforms provide the ability to natively execute mathematical and analytical
models in the database queries, as well as their own extended languages and
models for advanced analysis. Prior to moving any workloads to a cloud EDW,
an analysis should be completed to identify workloads that will require their
analytical models to be updated to languages supported by the cloud provider
and cloud EDW platform
Figure 3 (Table): Cloud Provider EDW Technologies
22 | THE DOPPLER | FALL 2016