The Impact of Columnar In-Memory Databases on Enterprise Systems Implications of Eliminating Transaction-Maintained Aggregates

Five years ago I proposed a common database approach for transaction processing and analytical systems using a colum-nar in-memory database, disputing the common belief that column stores are not suitable for transactional workloads. Today, the concept has been widely adopted in academia and industry and it is proven that it is feasible to run analyti-cal queries on large data sets directly on a redundancy-free schema, eliminating the need to maintain pre-built aggre-gate tables during data entry transactions. The resulting re-duction in transaction complexity leads to a dramatic simpli-fication of data models and applications, redefining the way we build enterprise systems. First analyses of productive applications adopting this concept confirm that system ar-chitectures enabled by in-memory column stores are concep-tually superior for business transaction processing compared to row-based approaches. Additionally, our analyses show a shift of enterprise workloads to even more read-oriented processing due to the elimination of updates of transaction-maintained aggregates. 1