Storage and Read-Optimized Data Placement Structures for High-Performance Analysis
Abstract
We present state-of-art structures and methods for efficient data preparation and representation for analysis. Our intent is to introduce the data science and analytics communities to open source data placements, structures, and methods. These practices can make the foundational processes of data preparation and access dramatically more efficient than typical raw file or database representations and use more conservative storage. To illustrate this, we introduce two highly efficient data placement structures. We then present a tutorial, supported by step-by-step examples, of how to create, use, and access data, structured by Parquet or ORC (Optimized Row Columnar), using Apache Spark. Finally, we illustrate the benefits of using these structures with computational and storage volume benchmarks.
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