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            <h1><strong>In-Memory Data Grid</strong> with Apache Ignite</h1>
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        <p>
            The Apache Ignite® in-memory data grid accelerates and scales your databases, services, 
            and APIs. It supports key-value and ANSI SQL APIs, ACID transactions, co-located processing, 
            and machine learning libraries. As an in-memory data grid, Ignite is frequently used to increase the performance and
            scalability of existing real-time applications, for <a href="/use-cases/high-performance-computing.html">
            high-performance computing</a>, or for caching of data scattered across databases.
        </p>
        <img class="img-fluid diagram-right" src="/images/svg-diagrams/data_grid.svg" alt="In-Memory Data Grid with Apache Ignite"/>
        
        <h2>What is an In-Memory Data Grid?</h2>

        <p>An in-memory data grid is a read-through/write-through caching strategy in which the application 
        layer treats the data grid as the primary data store. Applications write to and read
        from the in-memory cluster that propagates all the changes to any underlying databases automatically keeping them
        updated and consistent with the in-memory data.</p>

        <p>Ignite as an in-memory data grid provides all essential APIs needed to simplify its adoption. The
        APIs include <a href="/use-cases/key-value-store.html">distributed key-value</a> and <a href="/features/sql.html">ANSI SQL</a> queries, <a href="/features/acid-transactions.html">ACID transactions</a>, <a href="/features/collocated-processing.html">co-located processing</a>, 
        and <a href="/features/machinelearning.html">machine learning</a> models. While key-value and SQL calls let you request, join, and group 
        distributed data sets, the compute and machine learning components help to eliminate data 
        shuffling over the network, thus, boosting compute and data-intensive calculations.</p>

        <p>Ignite can store data both in memory and on disk with two options for data persistence. You 
        can persist changes in an external database or have Ignite keep data in its <a href="/arch/native-persistence.html">native persistence</a>. </p>

        <h2>IGNITE as an in-memory data grid AND EXTERNAL DATABASES</h2>

        <p>Ignite can improve the performance and scalability of any external
            database such as RDBMS, NoSQL or Hadoop, by sliding in as an in-memory data grid between the
            application and the database layer. When an application writes data to the cache, Ignite 
            automatically writes-through or writes-behind all data modifications to the underlying 
            external store. Ignite also performs ACID transactions where it coordinates and commits 
            a transaction across the cluster as well as the database.</p>

        <p>Additionally, Ignite can be deployed as a shared and unified in-memory layer that stores 
            data sets originating from disjointed databases. Your applications can consume all the 
            data from Ignite as a single store while Ignite can keep the original databases in sync 
            whenever in-memory data gets updated.</p>

        <p>However, there are some limitations if an external database is used as a persistence 
            layer for Ignite in-memory data grid deployments. For instance, if you run Ignite SQL 
            or scan queries, you need to ensure that all data is preloaded to the in-memory cluster. 
            Note that Ignite SQL or scan queries can read data from disk only if the data is stored 
            in the Ignite native persistence.</p>

        <h2>IGNITE NATIVE PERSISTENCE</h2>

        <p>Ignite native persistence is a distributed ACID and SQL-compliant disk store that 
            transparently integrates with the Ignite in-memory layer. With native persistence 
            enabled, Ignite stores both data and indexes on disk and eliminates the time-consuming 
            cache warm-up step. The native persistence always keeps a full copy of data on disk so 
            you are free to cache a subset of records in memory. If a required data record is missing 
            in memory, Ignite reads it from the disk automatically regardless of the API, whether 
            you use SQL, key-value, or scan queries.</p>


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                                    Distributed SQL <i class="fas fa-angle-double-right"></i>
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                                    Co-located Processing <i class="fas fa-angle-double-right"></i>
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                                ACID Transactions <i class="fas fa-angle-double-right"></i>
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                                Machine and Deep Learning <i class="fas fa-angle-double-right"></i>
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                                    Native Persistence <i class="fas fa-angle-double-right"></i>
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                            <li><a href="/use-cases/in-memory-database.html">
                                Ignite as an In-Memory Database <i class="fas fa-angle-double-right"></i>
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                            <li><a href="/use-cases/digital-integration-hub.html">
                                Ignite as a Digital Integration Hub <i class="fas fa-angle-double-right"></i>
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