Modelling and Querying Star and Snowflake Warehouses Using Graph Databases

Abstract

In current “Big Data” scenarios, graph databases are increasingly being used. Online Analytical Processing (OLAP) operations can expand the possibilities of graph analysis beyond the traditional graph-based computation. This paper studies graph databases as an alternative to implement star and snowflake schemas, the typical choices for data warehouse design. For this, the MusicBrainz database is used. A data warehouse for this database is designed, and implemented over a Postgres relational database. This warehouse is also represented as a graph, and implemented over the Neo4j graph database. A collection of typical OLAP queries is used to compare both implementations. The results reported here show that in ten out of thirteen queries tested, the graph implementation outperforms the relational one, in ratios that go from 1.3 to 26 times faster, and performs similarly to the relational implementation in the three remaining cases.