The integration of heterogeneous data sources is one of the main challenges within the area of data engineering. Due to the absence of an independent and universal benchmark for data-intensive integration processes, we propose a scalable benchmark, called DIPBench (Data intensive integration Process Benchmark), for evaluating the performance of integration systems. This benchmark could be used for subscription systems, like replication servers, distributed and federated DBMS or message-oriented middleware platforms like Enterprise Application Integration (EAI) servers and Extraction Transformation Loading (ETL) tools. In order to reach the mentioned universal view for integration processes, the benchmark is designed in a conceptual, process-driven way. The benchmark comprises 15 integration process types. We specify the source and target data schemas and provide a toolsuite for the initialization of the external systems, the execution of the benchmark and the monitoring of the integration system's performance. The core benchmark execution may be influenced by three scale factors. Finally, we discuss a metric unit used for evaluating the measured integration system's performance, and we illustrate our reference benchmark implementation for federated DBMS.