Mixed Workload Management
Create business agility.
Data volumes are exploding and yet competitive pressures, customer demands and the global economy require sophisticated processes that leverage both historical and detailed data to stay ahead. As the “Google generation” of consumers enters the market, businesses must improve the use of analytic insight across a wide range of business users.
Business Requirements Drive Complexity
| User Profile | Application/Tools | Workload | |
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Parameterized Reporting | ![]() |
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Business Metrics | ||
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Interactive Aggregations | |
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Ad hoc Queries | |
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Historical Trend Analysis | |
| Foundation Layer: Shared Data, Different Usages | |||
In order to leverage this data, business users need self-service data access for a variety of analysis methods using a broad mix of applications and tools. While each of the workloads outlined above are very different, they all share one common factor, data.
The Dataupia Satori Server is uniquely architected to handle a broad mix of data warehouse workloads. These can include support for such diverse activities as data mining, aggregation and executing point queries as shown in the graphic below. Dataupia ‘s solution is a new class of data warehouse appliance – built from the ground up to deliver a mainstream data warehouse solution that serves a large number of business users across a spectrum of query needs.

With the Dataupia Satori Server you can:
- Keep business-relevant data readily accessible with minimal overhead.
- Securely collect, store, and query massive amounts of data cost-effectively.
- Augment current investments in technology to better serve your need to deliver more data, more quickly.
- Implement multidimensional aggregates that provide extraordinary load rates, allowing data to refresh at operational rates of every 15 seconds.
Learn more
Get all the facts about the Dataupia Satori Server, a new class of data warehouse appliance.
Dataupia Satori Server Brochure
Dataupia Satori Server with Dynamic Aggregation Datasheet

Product Overview 