Additional contribution by: Santanu Chatterjee, Trystan Leftwich, Bryan Naden.
A new and powerful method of computing percentile estimates on Big Data is now available to you! By combining the well known t-Digest algorithm with AtScale’s semantic layer and smart aggregation features AtScale addresses gaps in both the Business Intelligence and Big Data landscapes. Most BI tools have features to compute and display various percentiles (i.e. medians, interquartile ranges, etc), but they move data for processing which dramatically limits the size of the analysis. The Hadoop-based SQL engines (Hive, Impala, Spark) can compute approximate percentiles on large datasets, however these expensive calculations are not aggregated and reused to answer similar queries. AtScale offers robust percentile estimates that work with AtScale’s semantic layer and aggregate tables to provide fast, accurate, and reusable percentile estimates.
In this three-part blog series we discuss the benefits of percentile estimates and how to compute them in a Big Data environment. Subscribe today to learn the best practices of percentile estimation on Big Data and more. Let's dive right in!
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