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From dma...@apache.org
Subject svn commit: r1803912 - /ignite/site/trunk/releases/2.1.0/release_notes.html
Date Wed, 02 Aug 2017 19:44:22 GMT
Author: dmagda
Date: Wed Aug  2 19:44:22 2017
New Revision: 1803912

URL: http://svn.apache.org/viewvc?rev=1803912&view=rev
Log:
corrected the release notes

Modified:
    ignite/site/trunk/releases/2.1.0/release_notes.html

Modified: ignite/site/trunk/releases/2.1.0/release_notes.html
URL: http://svn.apache.org/viewvc/ignite/site/trunk/releases/2.1.0/release_notes.html?rev=1803912&r1=1803911&r2=1803912&view=diff
==============================================================================
--- ignite/site/trunk/releases/2.1.0/release_notes.html (original)
+++ ignite/site/trunk/releases/2.1.0/release_notes.html Wed Aug  2 19:44:22 2017
@@ -20,15 +20,15 @@
 
   Relying on this new feature and on advanced SQL capabilities which existed before, Ignite
can serve as a distributed transactional SQL database, both in memory and on disk, while continuing
to support all the existing use cases, including the in-memory data grid. </p>
 <h2>Data Definition Language</h2>
-<p class="description">Data Definition Language support was just announced around a
month ago with the ability to create and drop SQL indexes in runtime and now you manage your
caches and SQL schema with commands like CREATE or DROP table. In general, it means that you
can connect to GridGain using JDBC or ODBC driver and fully configure the cluster using those
well-know DDL statements. There is no more need to deal with Spring XML, Java or .NET specific
configurations options for your cluster.</p>
+<p class="description">Data Definition Language support was just announced around a
month ago with the ability to create and drop SQL indexes in runtime and now you manage your
caches and SQL schema with commands like CREATE or DROP table. In general, it means that you
can connect to Ignite using JDBC or ODBC driver and fully configure the cluster using those
well-know DDL statements. There is no more need to deal with Spring XML, Java or .NET specific
configurations options for your cluster.</p>
 <h2>Machine Learning</h2>
 <p class="description">Machine Learning Grid becomes more powerful with an addition
of distributed versions of such widely used algorithms us logistic regressions, linear regression
and k-mean clustering. Furthermore, the foundation of the component which is distributed algebra
has been significantly optimized and boosted to get everything from the hardware available
cluster wide.</p>
 <h2>.NET</h2>
-<p class="description">Java part of GridGain supports peer-class loading feature for
a while. In short, with this feature enabled you don't have to manually deploy your Java or
Scala code on each node in the cluster and re-deploy it each time it changes. The required
classes will be preloaded or removed whenever is needed.
+<p class="description">Java part of Ignite supports peer-class loading feature for
a while. In short, with this feature enabled you don't have to manually deploy your Java or
Scala code on each node in the cluster and re-deploy it each time it changes. The required
classes will be preloaded or removed whenever is needed.
 
-Starting with GridGain 8.1.1 this feature is no longer a privilege of Java users. Now .NET
developers can benefit from the same - a .NET assembly can be automatically preloaded to an
already running .NET cluster node if an implementation of a distributed computation task is
missing locally. The unloading is handled for you as well.</p>
+Starting with Ignite 2.1 this feature is no longer a privilege of Java users. Now .NET developers
can benefit from the same - a .NET assembly can be automatically preloaded to an already running
.NET cluster node if an implementation of a distributed computation task is missing locally.
The unloading is handled for you as well.</p>
 <h2>C++</h2>
-<p class="description">Compute Grid support is expanded to C++ level. Now nothing can
prevents you from designing and developing compute tasks using C++ language and send the tasks
for the execution to a GridGain cluster - Ignite.C++ will be able to serialize, deserialize
and run the computations for you.</p>
+<p class="description">Compute Grid support is expanded to C++ level. Now nothing can
prevents you from designing and developing compute tasks using C++ language and send the tasks
for the execution to a Ignite cluster - Ignite.C++ will be able to serialize, deserialize
and run the computations for you.</p>
 </div>
 <h2>Features and Improvements</h2>
 <ul>



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