The organizations are rapidly looking for ways to move to cloud services. While deciding workload placement, the highest priority should be to understand your organization’s business needs and pain points. These are the different kind of workloads of Decision Support Systems (DSS) that need to be considered.
- Includes technologies and architectures to process and analyze very large volumes of data, for a wide variety of types of structured and unstructured data.
- Enables high-velocity capture, discovery, high-performance, and high-end commercial analytics, and simulation to uncover hidden patterns, unknown correlations, and other useful information.
- Includes embedded workloads associated with application data store, smart data, fast data, and massive data.
- Includes both discrete tasks and those often embedded in other workloads such as economic/financial, industrial, scientific research, etc.
Data Mining/Data Analytics
- Involves infrastructure and tools that are used to access data warehouses for online analytical processing (OLAP), data visualization, data mining, web query tools etc.
- Includes orthogonal tasks embedded into the data analysis/data mining workload.
- Economics/financial workloads and data analysis/data mining workloads that fit the big data definition are excluded.
Data Warehouse/Data Mart
- Considered a core component of business intelligence, these are central repositories of integrated data from one or more disparate sources, used for reporting and data analysis.
- Includes discrete workloads associated with application data store, smart data, fast data, and massive data.
- Includes econometric modeling, portfolio management, stock market and economic forecasting, and financial analysis.
- Includes both trader and computationally intensive non-trader tasks and economics/financial workloads that fit the technical computing definition.
Includes orthogonal tasks embedded into economics/financial workload.