Today, one of the major challenges in science is the processing of large data sets. Experiments or simulations can produce an enormous amount of intermation that is stored in databases or files. Processing these data is usually done by splitting the analysis process into a large number of small jobs that read only limited fragments of the data and process them in parallel on a Grid. Examples of such a scenario are the processing of images in medicine, gene alignment in bioinformatics, or the analysis of high energy physics data. The Large Hadron Collider (LHC) at CERN will provide particle physicists with several petabytes of experimental data every year. Additionally, simulations of the physics processes and the detector response are needed to understand the experiment. With the LHC Computing Grid (LCG), the High Energy Physics community wants to provide a Grid environment to enable such data processing capabilities. The gLite middleware stack is the base of this Grid effort. The High Energy Particle Physics Community Grid project (HEPCG) of the German D-Grid Initiative wants to contribute to the functionality that the LCG provides to users in the areas of distributed data management, job monitoring and distributed data analysis.