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| <h1 class="h1 innerhero__h1">Accelerate Existing Hadoop Deployments</h1> |
| <div class="h3 pt-2 with-apache">With Apache Ignite</div> |
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| Achieve the performance acceleration of Hadoop-based<br /> |
| application with Ignite as a high-performance data access<br /> |
| layer |
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| <h2 class="compute2__h2">Benefits Of Using Apache Ignite</h2> |
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| <h3 class="fz20 compute2item__title">Real-time analytics</h3> |
| <p class="compute2__text base2__text">Apache Ignite enables real-time analytics across Apache Hadoop operational and historical data silos.</p> |
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| <h3 class="fz20 compute2item__title">Low-latency and high-throughput operations</h3> |
| <p class="compute2__text base2__text">Ignite enables low-latency and high-throughput access while Hadoop continues to be used for long-running OLAP workloads.</p> |
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| <h2 class="doop3__h2 h5">How Does Apache Ignite Acceleration Work?</h2> |
| <p class="doop3__text"> |
| To achieve the performance acceleration of Hadoop-based systems, deploy Ignite as a separate distributed storage that maintains the data sets required for your low-latency operations or real-time reports |
| </p> |
| <h2 class="doop3__h2 h5">There are 3 basic steps:</h2> |
| <div class="fz20 doop3__number">01</div> |
| <p class="doop3__subtext"> |
| Depending on the data volume and available memory capacity, you can enable<a href="https://ignite.apache.org/arch/native-persistence.html" target="_blank"> Ignite native persistence</a> to store historical data sets on disk |
| while dedicating a memory space for operational records. |
| </p> |
| <p class="doop3__subtext pt-1">You can continue to use Hadoop as storage for less frequently used data or for long-running and ad-hoc analytical queries.</p> |
| <div class="fz20 doop3__number">02</div> |
| <p class="doop3__subtext"> |
| Your applications and services should use Ignite native APIs to process the data residing in the in-memory cluster. Ignite provides SQL, compute (aka. map-reduce), and machine learning APIs for various data processing needs. |
| </p> |
| <div class="fz20 doop3__number">03</div> |
| <p class="doop3__subtext">Consider using Apache Spark DataFrames APIs if an application needs to run federated or cross-database queries across Ignite and Hadoop clusters.</p> |
| <p class="doop3__subtext pt-1"> |
| Ignite is <a href="https://ignite.apache.org/use-cases/spark-acceleration.html" target="_blank">integrated with Spark</a>, which natively supports Hive/Hadoop. Cross-database queries should be considered only for a limited |
| number of scenarios when neither Ignite nor Hadoop contains the entire data set. |
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| <h2 class="doop4__h2 h4">How Can You Split Data And Operations Between Ignite And Hadoop?</h2> |
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| <p class="doop4__text"> |
| Use Apache Ignite for tasks that require:<br /> |
| – Low-latency response time <span class="doop4__grey">(microseconds, milliseconds, seconds)</span> |
| </p> |
| <p class="doop4__text pt-1">– High throughput operations <span class="doop4__grey">(thousands and millions of operations per second)</span> <br />– Real-time processing.</p> |
| </div> |
| <div class="doop4__item"> |
| <p class="doop4__text">Continue using Apache Hadoop for: <br />— High-latency operations <span class="doop4__grey">(dozens of seconds, minutes, hours)</span><br />— Batch processing</p> |
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| <h2 class="h4 doop5__h2">5 Steps To Implement The Architecture In Practice</h2> |
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| <div class="doop5__item post1"> |
| <div class="doop5__number h4">01</div> |
| <h4 class="doop5__title">Download and install Apache Ignite to your system.</h4> |
| </div> |
| <div class="doop5__item post2"> |
| <div class="doop5__number h4">02</div> |
| <h4 class="doop5__title">Select a list of operations for Ignite.</h4> |
| <p class="doop5__text pt-2">The best operations are those that require low-latency response time, high-throughput, and real-time analytics.</p> |
| </div> |
| <div class="doop5__item post3"> |
| <div class="doop5__number h4">03</div> |
| <p class="doop5__text"> |
| <span class="doop5__title">Consider enabling Ignite native persistence,</span> or use Ignite as a pure in-memory cache, or in-memory data grid that persists changes to Hadoop or another external database. |
| </p> |
| </div> |
| <div class="doop5__item post4"> |
| <div class="doop5__number h4">04</div> |
| <h4 class="doop5__title">Update your applications</h4> |
| <p class="doop5__text pt-2">Ensure they use Ignite native APIs to process Ignite data and Spark for federated queries.</p> |
| </div> |
| <div class="doop5__item post5"> |
| <div class="doop5__number h4">05</div> |
| <div class="doop5__titleend">If you need to replicate changes between Ignite and Hadoop clusters, use existing change-data-capture solutions:</div> |
| <div class="doop5__part flexi"> |
| <p>Debezium<br />Kafka</p> |
| <p class="doop5__middle">GridGain Data Lake Accelerator<br />Oracle GoldenGate</p> |
| <p class="doop5__end"> |
| To write-through changes to Hadoop directly,<br /> |
| implement <a href="https://ignite.apache.org/docs/latest/persistence/external-storage" target="_blank">Ignite's CacheStore</a> interface. |
| </p> |
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| Discover our quick start guide and build your first<br /> |
| application in 5-10 minutes |
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| <a class="nativebotblock__link arrowlink" href="https://ignite.apache.org/docs/latest/" target="_blank">Quick Start Guide</a> |
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| <p class="nativebotblock__text">Read the Apache Spark acceleration article</p> |
| <a class="nativebotblock__link arrowlink" href="https://ignite.apache.org/use-cases/spark-acceleration.html" target="_blank">Apache Spark Acceleration</a> |
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