and must be maintained to ensure the legal compliance of the organization .
Organizational Reporting – Organizational reporting workloads are commonly exposed through KPIs that management uses to measure company performance . These workloads are often a good fit for a cloud EDW because they are run at scheduled times during the day and have a uniform data set , which they analyze for each execution of the jobs .
Sustained – Sustained workloads are commonly used by company management during the day for gathering reports and checking into key indicators for CRM , customer satisfaction , call center reporting and other metrics used to drive more tactical business decisions .
Variable – Variable workloads are those operational and financial reporting activities that an organization can plan for , although they do not continuously use EDW resources . These workloads are commonly used for planning sales teams ’ compensation and sales territories . Such workloads are good fits for cloud EDW .
Business Unit Specific – Business Unit workloads are commonly workloads that are part of a departmental data mart , or provide organization specific reporting . These are often the first workloads to move to cloud EDW , because individual departments are less dependent on corporate IT and have complete access to their own data sets .
The above categories help to define the lowest risk , easiest technical use cases and workloads to migrate to a cloud EDW . This initial migration will provide the minimum capabilities to enable the future migration of use cases with higher technical needs and risk .
Moving to an EDW platform in the cloud involves several important design and migration considerations .
These are key to ensuring that the EDW functionality moved to the cloud is seamlessly integrated with workloads that will stay on premise , and that downtime is minimized . Each of the following categories should contribute to driving the target architecture for a cloud based EDW , and to determining the priority of workloads that will move to the cloud .
Movement of Data to the Cloud – Moving data between facilities , especially at volume , can be a time-consuming process . When migrating an EDW , it is important to define the data sets and volumes early , so that proper connectivity can be enabled for data movement , and a project schedule accurately built for the data migration time frames .
Data Integration & Access – Movement to the cloud will require ETL processes and data flows to be extended beyond the on-premise implementation . ETL tools should be validated for cloud operation , support and proper features for integration with cloud-native EDW technologies .
Data Transit Costs – Data movement costs from a cloud provider to on-premise deployments can add up quickly if data is not moved efficiently . Because cloud providers charge for data egress , ETL processes and data migration must
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