We present Fuzzy Prophet, a probabilistic database tool for constructing, simulating and analyzing business scenarios with uncertain data. Fuzzy Prophet takes externally defined probability distribution (so called VG-Functions) and a declarative description of a target scenario, and performs Monte Carlo simulation to compute probability distribution of the scenario's outcomes. In addition, Fuzzy Prophet supports parameter optimization,where probabilistic models are parameterized and a large parameter space must be explored to find parameters that optimize or achieve a desired goal. Fuzzy Prophet's key innovation is to use 'fingerprints' that can identify parameter values producing correlated outputs of a user-provided stochastic function and to reuse computations across such values. Fingerprints significantly expedite the process of parameter exploration in offline optimization and interactive what-if exploration tasks.

Contemporary datacenters house tens of thousands of servers. The servers are closely monitored for operating conditions and utilizations by collecting their performance data (e.g., CPU utilization). In this paper, we show that existing database and file-system solutions are not suitable for warehousing performance data collected from a large number of servers because of the scale and the complexity of performance data. We describe the design and implementation of DataGarage, a performance data warehousing system that we have developed at Microsoft. DataGarage is a hybrid solution that combines benefits of DBMSs, file-systems, and MapReduce systems to address unique challenges of warehousing performance data. We describe how DataGarage allows efficient storage and analysis of years of historical performance data collected from many tens of thousands of servers---on commodity servers. We also report DataGarage's performance with a real dataset and a 32-node, 256-core shared-nothing cluster and our experience of using DataGarage at Microsoft for the last one year.