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Many popular graph databases are optimized to run on a single machine, using efficient traversals to query the stored graphs. This boosts performance of algorithms originating at a single vertex and iterating through the graph e.g. finding shortest paths or neighbors. However, graphs are getting bigger and traversals are poorly performing if they require a large depth. If you need to distribute a large-scale graph thru several machines, traversals won't be the best choice (in case of performance) to process the graph. Therefore Google has released it's Pregel framework offering an environment to query distributed graphs, Pregel is also known as the map-reduce for graphs. In this talk I want to present the architecture and requirements of the Pregel framework and introduce you to the different mind-set required to write a Pregel algorithm. Furthermore I will give a short introduction to three implementations or Pregel — Giraph, TinkerPop3 and ArangoDB.