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          content="Apache Ignite enables real-time analytics across operational and historical silos for existing
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            <h1>Apache Hadoop <strong>Performance Acceleration</strong></h1>
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    <div class="container">
        <p>
            Apache Ignite® enables real-time analytics across Apache™ Hadoop® operational and historical data silos. The
            Ignite in-memory computing platform provides low-latency and high-throughput operations while Hadoop
            continues to be used for long-running OLAP workloads.
        </p>
        <img class="diagram-right img-fluid" src="/images/svg-diagrams/hadoop_acceleration.svg" alt="Apache Hadoop Performance Acceleration" />

        <p>
            As the architecture diagram on the right suggests, you can achieve the performance acceleration
            of Hadoop-based systems by deploying Ignite as a separate distributed storage that maintains the data
            sets required for your low-latency operations or real-time reports.
        </p>

        <p>
            First, depending on the data volume and available memory capacity, you can enable Ignite native persistence
            to
            store historical data sets on disk while dedicating a memory space for operational records. You can
            continue to use Hadoop as storage for less frequently used data or for long-running and ad-hoc
            analytical queries.
        </p>

        <p>
            Next, 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>

        <p>
            Finally, consider using Apache Spark DataFrames APIs if an application needs to run federated or
            cross-database queries across Ignite and Hadoop clusters. Ignite is integrated with Spark, 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.
        </p>


        <h2>How to split data and operations between Ignite and Hadoop?</h2>
        <p>
            Consider using this approach:
        </p>
        <ul>
            <li>
                Use Apache Ignite for tasks that require low-latency response time (microseconds,
                milliseconds, seconds), high throughput operations (thousands and millions of
                operations per second), and real-time processing.
            </li>
            <li>
                Continue using Apache Hadoop for high-latency operations (dozens of seconds, minutes, hours) and
                batch processing.
            </li>
        </ul>


        <h2>Getting Started Checklist</h2>
        <p>
            Follow the steps below to implement the discussed architecture in practice:
        </p>
        <ul>
            <li>
                Download and install Apache Ignite in your system.
            </li>
            <li>
                Select a list of operations/reports to be executed against Ignite. The best candidates are
                operations that require low-latency response time, high-throughput, and real-time analytics.
            </li>
            <li>
                Depending on the data volume and available memory space, consider using Ignite native
                persistence. Alternatively, you can use Ignite as a pure in-memory cache or in-memory data grid
                that persists changes to Hadoop or another external database.
            </li>
            <li>
                Update your applications to ensure they use Ignite native APIs to process Ignite data and Spark
                for federated queries.
            </li>
            <li>
                If you need to replicate changes between Ignite and Hadoop clusters, consider using existing
                change-data-capture solutions like Debezium, Kafka, GridGain Data Lake Accelerator, Oracle GoldenGate
                or others. If you'd like Ignite to write-through changes to Hadoop directly, then implement
                <a href="https://apacheignite.readme.io/docs/3rd-party-store" target="_blank">Ignite's CacheStore</a>
                interface.
            </li>
        </ul>


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                                    Co-located Processing <i class="fa fa-angle-double-right"></i>
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                                ACID Transactions <i class="fa fa-angle-double-right"></i>
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                                    Ignite as an In-Memory Data Grid <i class="fa fa-angle-double-right"></i>
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                                Ignite as an In-Memory Database <i class="fa fa-angle-double-right"></i>
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                                Ignite as a Digital Integration Hub <i class="fa fa-angle-double-right"></i>
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