As data volumes continue to expand, analytics approaches that require exhaustively scanning data sets become untenable. For this reason, modern analytics systems employ data skipping techniques to avoid looking at large volumes of irrelevant data. By maintaining some metadata for each block of data, a query may skip a data block if the metadata indicates that the block does not contain relevant data. The effectiveness of data skipping, however, depends on how the underlying data are organized into blocks.

In this dissertation, we propose a fine-grained data layout framework, called ``Generalized Skipping-Oriented Partitioning and Replication'' (GSOP-R), which aims to maximize query performance through aggressive data skipping. Based on observations of real-world analytics workloads, we find that the workload patterns can be summarized as a succinct set of features. The GSOP-R framework uses these features to transform the incoming data into a small set of feature vectors, and then performs clustering algorithms using the feature vectors instead of the actual data. A resulting GSOP-R layout scheme is highly flexible. For instance, it allows different columns to be horizontally partitioned in different ways and supports replication of only parts of rows or columns.

We developed several designs for GSOP-R on Apache Spark and Apache Parquet and then evaluated their performance using two public benchmarks and several real-world workloads. Our results show that GSOP-R can reduce the amount of data scanned and improve end-to-end query response times over the state-of-the-art techniques by a factor of 2 to 9.