Aggregation queries are at the core of business intelligence and data analytics. In the big data era, many scalable shared-nothing systems have been developed to process aggregation queries over massive amount of data. Microsoft’s SCOPE is a well-known instance in this category. Nevertheless, aggregation queries are still expensive, because query processing needs to consume the entire data set, which is often hundreds of terabytes. Data sampling is a technique that samples a small portion of data to process and returns an approximate result with an error bound, thereby reducing the query’s execution time. While similar problems were studied in the database literature, we encountered new challenges that disable most of prior efforts: (1) error bounds are dictated by end users and cannot be compromised, (2) data is sparse, meaning data has a limited population but a wide range. For such cases, conventional uniform sampling often yield high sampling rates and thus deliver limited or no performance gains. In this paper, we propose error-bounded stratified sampling to reduce sample size. The technique relies on the insight that we may only reduce the sampling rate with the knowledge of data distributions. The technique has been implemented into Microsoft internal search query platform. Results show that the proposed approach can reduce up to 99% sample size comparing with uniform sampling, and its performance is robust against data volume and other key performance metrics.