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Apache Fluo - Large-scale Incremental Processing
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These workflows enable users to continuously join new data into large existing data sets without reprocessing all data. Apache Fluo is built on <a href="https://accumulo.apache.org/" target="_blank">Apache Accumulo</a>.</p> <p>Take the <a href="/tour/">Fluo tour</a> if you are interested in learning more. Feel free to <a href="/contactus/">contact us</a> if you have questions.</p> </div> <div class="col-sm-4"> <div class="row"> <div class="col-sm-12 panel panel-default"> <h3 id="news-header">Latest News</h3> <div class="post-header-home"> <div class="row"> <div class="col-sm-12"> <p><a href="/release/2.0.0/">Apache Fluo 2.0.0</a> <small class="text-muted">Apr 2023</small></p> </div> </div> </div> <div class="post-header-home"> <div class="row"> <div class="col-sm-12"> <p><a href="/blog/2019/09/30/scan-executors/">How Fluo Leveraged Scan Executors</a> <small class="text-muted">Sep 2019</small></p> </div> </div> </div> <div class="post-header-home"> <div class="row"> <div class="col-sm-12"> <p><a href="/release/fluo-yarn-1.0.0/">Apache Fluo YARN 1.0.0</a> <small class="text-muted">Mar 2018</small></p> </div> </div> </div> <div class="post-header-home"> <div class="row"> <div class="col-sm-12"> <p><a href="/release/fluo-recipes-1.2.0/">Apache Fluo Recipes 1.2.0</a> <small class="text-muted">Mar 2018</small></p> </div> </div> </div> <div class="post-header-home"> <div class="row"> <div class="col-sm-12"> <p><a href="/release/fluo-1.2.0/">Apache Fluo 1.2.0</a> <small class="text-muted">Feb 2018</small></p> </div> </div> </div> <div id="news-archive-link"> View all posts in the <a href="/news/">news archive</a> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <a class="acevent" data-class="img-responsive center-block" data-format="wide" data-mode="light" data-width="240"></a> </div> </div> </div> </div> <h3 style="padding-top:0px;">Major Features</h3> <div class="row"> <div class="col-sm-4"> <h4>Reduced Latency</h4> <p>When combining new data with existing data, Fluo offers reduced latency when compared to batch processing frameworks (e.g Spark, MapReduce).</p> </div> <div class="col-sm-4"> <h4>Reliable</h4> <p>Incremental updates are implemented using transactions which allow thousands of updates to happen concurrently without corrupting data.</p> </div> <div class="col-sm-4"> <h4>Core API</h4> <p>The core <a href="/docs/fluo/1.2/">Fluo API</a> supports simple, cross-node transactional updates using get/set methods.</p> </div> </div> <div class="row"> <div class="col-sm-4"> <h4>Avoid Reprocessing Data</h4> <p>Combine new data with existing data without having to reprocess the entire dataset.</p> </div> <div class="col-sm-4"> <h4>General Purpose</h4> <p>Fluo applications consist of a series of observers that execute user code when observed data is updated.</p> </div> <div class="col-sm-4"> <h4>Recipes API</h4> <p>The <a href="/docs/fluo-recipes/1.2/">Fluo Recipes API</a> builds on the core API to offer complex transactional updates.</p> </div> </div> </div> </div> <div class="row"> <div class="col-sm-12 center-block"> <footer> <hr/> <p> <a href="https://www.apache.org/foundation/contributing"><img src="https://www.apache.org/images/SupportApache-small.png" id="asf-logo" height="100" alt="Apache"/></a> </p> <p> Copyright © 2024 <a href="https://www.apache.org">The Apache Software Foundation</a>. 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