NoSQL: Druid: Distributed In-Memory OLAP Data Store

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Druid: Distributed In-Memory OLAP Data Store

Over the last twelve months, we tried and failed to achieve scale and speed with relational databases (Greenplum, InfoBright, MySQL) and NoSQL offerings (HBase). Stepping back from our two failures, let's examine why these systems failed to scale for our needs: 1. Relational Database Architectures - Full table scans were slow, regardless of the storage engine used - Maintaining proper dimension tables, indexes and aggregate tables was painful - Parallelization of queries was not always supported or non-trivial 2. Massive NOSQL With Pre-Computation - Supporting high dimensional OLAP requires pre-computing an exponentially large amount of data

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