license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. layout: home title: “Home” section-1: title: | The first system enabling automatic __________ big data cross-platform processing text: | Apache Wayang is an API-first system designed to fully support cross-platform data processing: Wayang enables users to run data analytics over multiple data processing platforms, whithout changing the native code. Wayang provides an abstraction on top of existing platforms in order to run data processing and analytic tasks on top of any set of platforms. As a result, users can focus on the logics of their applications rather on the intricacies of the underlying platforms. section-2: title: Turning shadows into a show text: | Read more on how Apache Wayang converts the light and shadows of data processing platforms to amazing theatre for you. section-3: title: Features text: How we move the strings for you section-4: title: Why is Apache Wayang faster as other, modern frameworks? text: | Apache Wayang uses internal optimization patterns to detect the best possible combination of computation and nodes. We know, just adding more nodes into a cluster doesn‘t mean more speed; each additional node has several tradeoffs, be it shuffle or communication bottlenecks. Apache Wayang understands the UDF’s and optimizes the function for the underlying processing platform. It also uses small JVM instances to reduce the operational overhead when processing a reduced number of data points.