One of the purposes of Big Data systems is to support analysis of data gathered from heterogeneous data sources. Since data warehouses have been used for several decades to achieve the same goal, they could be leveraged also to provide analysis of data stored in Big Data systems. The problem of adapting data warehouse data and schemata to changes in these requirements as well as data sources has been studied by many researchers worldwide. However, innovative methods must be developed also to support evolution of data warehouses that are used to analyze data stored in Big Data systems. In this paper, we propose a data warehouse architecture that allows to perform different kinds of analytical tasks, including OLAP-like analysis, on big data loaded from multiple heterogeneous data sources with different latency and is capable of processing changes in data sources as well as evolving analysis requirements. The operation of the architecture is highly based on the metadata that are outlined in the paper.