2LPA-RTDW: A Two-Level data Partitioning Approach for Real-time Data Warehouse

In order to explore the most recent data and react faster to changes of business conditions, organizations consider Real-Time Data Warehousing (RTDW) as a powerful technique to achieve OLAP (On Line Analytical Processing) analyses and business intelligence (BI). OLAP analyses are complex since they query several relational tables with huge volumes. In order to deal with this volumetry, several optimization techniques have been proposed in the literature as materialized views and data partitioning. Partitioning is an effective method to increase query efficiency in a data warehouse. This paper proposes a novel data partitioning approach for real-time data warehouse, called 2LPA-RTDW (Two-Level data Partitioning Approach for Real-Time Data Warehouse) by allowing unbalance of data amount in each partition while taking into account user requirements. We have evaluated the proposed approach using the new TPC-DS¹ benchmark; the preliminary results show that the approach is quite interesting.