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There are many Big Data problems whose output is also Big Data. In this presentation we will show Splout SQL, which allows serving an arbitrarily big dataset by partitioning it. Splout serves ...
There are many Big Data problems whose output is also Big Data. In this presentation we will show Splout SQL, which allows serving an arbitrarily big dataset by partitioning it. Splout serves partitioned SQL views which are generated and indexed by Hadoop. Splout is to Hadoop + SQL what Voldemort or Elephant DB are to Hadoop + Key/Value. Hadoop is nowadays the de-facto open-source solution for Big Data batch-processing. When the output of a Hadoop process is big, there isn`t a satisfying solution for serving it. Think of pre-computed recommendations, for example, where the whole dataset may vary from one day to another. Splout decouples database creation from database serving and makes it efficient and safe to deploy Hadoop-generated datasets. There are many databases that allow serving Big Data such as NoSQL solutions, but they don`t have a rich query language like SQL. You generally can`t aggregate data in real-time like you would do with a GROUP BY clause. Because you can`t precompute everything, SQL is a very convenient feature to have in a Big Data serving solution. Splout is not a “fast analytics” engine. Splout is made for demanding web or mobile applications where query performance is critical. Arbitrary real-time aggregations should be done in less than 200 milliseconds under high traffic load. On top of that, Splout is scalable, flexible, RESTful & open-source.