svn commit: r1875735 - /ignite/site/branches/ignite-redisign/in-memory-computing.html

[dm...@apache.org]

Author: dmagda
Date: Thu Mar 26 18:45:44 2020
New Revision: 1875735

URL: http://svn.apache.org/viewvc?rev=1875735&view=rev
Log:
updating in-memory computing pages

Modified:
    ignite/site/branches/ignite-redisign/in-memory-computing.html

Modified: ignite/site/branches/ignite-redisign/in-memory-computing.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/in-memory-computing.html?rev=1875735&r1=1875734&r2=1875735&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/in-memory-computing.html (original)
+++ ignite/site/branches/ignite-redisign/in-memory-computing.html Thu Mar 26 18:45:44 2020
@@ -62,23 +62,23 @@ under the License.
     </header>
     <div class="container">
         <p>
-            In-memory computing is a type of software and data-processing technique that allows storing data sets in
-            memory, across a cluster of interconnected nodes, and process that data in parallel 100-1000x faster in
-            comparison to disk-based systems. Usually, in-memory computing software comprises of a distributed in-memory
-            store with APIs and libraries designed and optimized for high-performance data processing. Each cluster node
-            (physical or virtual machine) contributes its available memory space with CPU cores to the total capacity of
-            the cluster. An application interacts with the cluster as a single unit letting the latter shield and manage
-            all the internals related to inter-nodes communication, data distribution, and queries processing. The
-            cluster
-            scales linearly and horizontally to meet the application's data volume and throughput goals.
+            In-memory computing is a software and data-processing technique that stores data sets in memory across a
+            cluster of interconnected nodes. The data is processed in parallel to deliver performance that is 100-1000x
+            faster than disk-based systems. In-memory computing software includes a distributed in-memory store with
+            APIs and libraries optimized for high-performance data processing. Each cluster node (physical or virtual
+            machine) contributes its available memory space with CPU cores to the total capacity of the cluster.
+            An application interacts with the cluster as a single unit letting the in-memory computing software shield
+            and manage all the internals related to inter-node communications, data distribution, and queries processing.
+            The cluster scales linearly and horizontally to meet the data volume and throughput goals
+            of the applications.
         </p>
         <p>
-            Apache Ignite is a horizontally scalable, fault-tolerant distributed in-memory computing platform used to
-            build real-time applications processing terabytes of data with in-memory speed. Ignite's distributed multi-tier
-            storage scales up and out across available memory and disk resources and can be configured to function as an
-            in-memory cache, in-memory data grid, or in-memory database. Its compute and data processing APIs are capable of
-            handling large data sets with minimal or no network utilization by applying affinity co-location techniques for data
-            and compute logic.
+            Apache Ignite® is a horizontally scalable, fault-tolerant, distributed in-memory computing platform.
+            You can use Ignite to build real-time applications processing terabytes of data at in-memory speeds.
+            The Ignite distributed, multi-tier storage scales up and out across available memory and disk resources.
+            Ignite can be configured to function as an in-memory cache, in-memory data grid, or in-memory database.
+            The Ignite compute and data processing APIs are capable of handling large data sets with minimal or no
+            network utilization by applying affinity co-location techniques for data and compute logic.
         </p>
 
         <div class="jumbotron jumbotron-fluid">