[ignite] branch master updated: Updating outdated readme file that now matches content of the website.

[dmagda]

This is an automated email from the ASF dual-hosted git repository.

dmagda pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/ignite.git


The following commit(s) were added to refs/heads/master by this push:
     new 38f658f  Updating outdated readme file that now matches content of the 
website.
38f658f is described below

commit 38f658f8d63761b26e9bccee21c873860b61f4bb
Author: Denis Magda <dma...@gridgain.com>
AuthorDate: Mon Apr 13 15:59:06 2020 -0700

    Updating outdated readme file that now matches content of the website.
---
 README.md | 75 ++++++++++++++-------------------------------------------------
 1 file changed, 16 insertions(+), 59 deletions(-)

diff --git a/README.md b/README.md
index 345f12a..0178868 100644
--- a/README.md
+++ b/README.md
@@ -1,87 +1,44 @@
 # Apache Ignite
 
-<a href="https://ignite.apache.org/";><img 
src="https://ignite.apache.org/images/logo3.png"; hspace="20"/></a>
+<a href="https://ignite.apache.org/";><img 
src="https://github.com/apache/ignite-website/blob/master/images/Ignite_tm_Logo_black_RGB.svg";
 hspace="20"/></a>
 
 <a 
href="https://ci.ignite.apache.org/viewType.html?buildTypeId=IgniteTests24Java8_RunAll&branch_IgniteTests24Java8=%3Cdefault%3E";><img
 
src="https://ci.ignite.apache.org/app/rest/builds/buildType:(id:IgniteTests24Java8_RunAll)/statusIcon.svg"/></a>
 
 
-## Getting Started
-
-For information on how to get started with Apache Ignite, please visit: 
[Getting Started][getting-started].
-
-## Full Documentation
-
-You can find the full Apache Ignite documentation here: [Full 
documentation][docs].
-
-
 ## What is Apache Ignite?
 
-[Apache Ignite][apache-ignite-homepage] is a memory-centric distributed 
<strong>database</strong>, <strong>caching</strong>,
- and <strong>processing</strong> platform for transactional, analytical, and 
streaming workloads delivering in-memory
- speeds at petabyte scale.
+[Apache Ignite][apache-ignite-homepage] is a horizontally scalable, 
fault-tolerant distributed in-memory computing platform for building real-time 
applications that can process terabytes of data with in-memory speed.
 
 <p align="center">
-    <a href="https://ignite.apache.org/whatisignite.html";>
-        <img src="https://ignite.apache.org/images/ignite_architecture.png"; 
width="400px"/>
+    <a href="https://ignite.apache.org";>
+        <img 
src="https://github.com/apache/ignite-website/blob/master/images/png-diagrams/ignite_cluster.png";
 width="400px"/>
     </a>
 </p>
 
-## Memory-Centric Storage
-Apache Ignite is based on distributed memory-centric architecture that 
combines the performance and scale of in-memory
-computing together with the disk durability and strong consistency in one 
system.
-
-The main difference between the memory-centric approach and the traditional 
disk-centric approach is that the memory
-is treated as a fully functional storage, not just as a caching layer, like 
most databases do.
-For example, Apache Ignite can function in a pure in-memory mode, in which 
case it can be treated as an
-In-Memory Database (IMDB) and In-Memory Data Grid (IMDG) in one.
-
-On the other hand, when persistence is turned on, Ignite begins to function as 
a memory-centric system where most of
-the processing happens in memory, but the data and indexes get persisted to 
disk. The main difference here
-from the traditional disk-centric RDBMS or NoSQL system is that Ignite is 
strongly consistent, horizontally
-scalable, and supports both SQL and key-value processing APIs.
+## Multi-Tiere Storage
 
-[Read More](https://ignite.apache.org/arch/memorycentric.html)
+Apache Ignite is designed to work with memory, disk, and Intel Optane as 
active storage tiers. The memory tier allows using DRAM and Intel® Optane™ 
operating in the Memory Mode for data storage and processing needs. The disk 
tier is optional with the support of two options -- you can persist data in an 
external database or keep it in the Ignite native persistence. SSD, Flash, HDD, 
or Intel Optane operating in the AppDirect Mode can be used as a storage device.
 
-## Ignite Persistence
+[Read More](https://ignite.apache.org/arch/multi-tier-storage.html)
 
-Ignite Native Persistence is a distributed, ACID, and SQL-compliant **disk 
store** that transparently integrates with
-Ignite memory-centric storage as an optional disk layer storing data and 
indexes on SSD,
- Flash, 3D XPoint, and other types of non-volatile storages.
+## Ignite Native Persistence
 
-With the Ignite Persistence enabled, you no longer need to keep all the data 
and indexes in memory or warm it
-up after a node or cluster restart because the Durable Memory is tightly 
coupled with persistence and treats
-it as a secondary memory tier. This implies that if a subset of data or an 
index is missing in RAM,
-the Durable Memory will take it from the disk.
-
-<p align="center">
-    <a href="https://apacheignite.readme.io/docs/distributed-persistent-store";>
-        <img 
src="https://ignite.apache.org/images/native_persistence.png?renew"; 
width="400px"/>
-    </a>
-</p>
+Even though Apache Ignite is broadly used as a caching layer on top of 
external databases, it comes with its native persistence - a distributed, ACID, 
and SQL-compliant disk-based store. The native persistence integrates into the 
Ignite multi-tier storage as a disk tier that can be turned on to let Ignite 
store more data on disk than it can cache in memory and to enable fast cluster 
restarts.
 
 [Read More](https://ignite.apache.org/arch/persistence.html)
 
 ## ACID Compliance
 Data stored in Ignite is ACID-compliant both in memory and on disk, making 
Ignite a **strongly consistent** system. Ignite transactions work across the 
network and can span multiple servers.
 
-[Read More](https://apacheignite.readme.io/docs/transactions)
-
-## Complete SQL Support
-Ignite provides full support for SQL, DDL and DML, allowing users to interact 
with Ignite using pure SQL without writing any code. This means that users can 
create tables and indexes as well as insert, update, and query data using only 
SQL. Having such complete SQL support makes Ignite a one-of-a-kind 
**distributed SQL database**.
-
-[Read More](https://apacheignite.readme.io/docs/distributed-sql)
-
-## Key-Value
-The in-memory data grid component in Ignite is a fully transactional 
**distributed key-value store** that can scale horizontally across 100s of 
servers in the cluster. When persistence is enabled, Ignite can also store more 
data than fits in memory and survive full cluster restarts.
+[Read More](https://ignite.apache.org/features/transactions.html)
 
-[Read More](https://apacheignite.readme.io/docs/data-grid)
+## ANSI SQL Support
+Apache Ignite comes with a ANSI-99 compliant, horizontally scalable, and 
fault-tolerant SQL engine that allows you to interact with Ignite as with a 
regular SQL database using JDBC, ODBC drivers, or native SQL APIs available for 
Java, C#, C++, Python, and other programming languages. Ignite supports all DML 
commands, including SELECT, UPDATE, INSERT, and DELETE queries as well as a 
subset of DDL commands relevant for distributed systems.
 
-## Collocated Processing
-Most traditional databases work in a client-server fashion, meaning that data 
must be brought to the client side for processing. This approach requires lots 
of data movement from servers to clients and generally does not scale. Ignite, 
on the other hand, allows for sending light-weight computations to the data, 
i.e. **collocating** computations with data. As a result, Ignite scales better 
and minimizes data movement.
+[Read More](https://ignite.apache.org/features/sql.html)
 
-[Read More](https://apacheignite.readme.io/docs/collocate-compute-and-data)
+## Machine Learning and High-Performance Computing
+[Apache Ignite Machine 
Learning](https://ignite.apache.org/features/machinelearning.html) is a set of 
simple, scalable, and efficient tools that allow building predictive machine 
learning models without costly data transfers. The rationale for adding machine 
and deep learning to Apache Ignite is quite simple. Today's data scientists 
have to deal with two major factors that keep ML from mainstream adoption.
 
-## Scalability and Durability
-Ignite is an elastic, horizontally scalable distributed system that supports 
adding and removing cluster nodes on demand. Ignite also allows for storing 
multiple copies of the data, making it resilient to partial cluster failures. 
If the persistence is enabled, then data stored in Ignite will also survive 
full cluster failures. Cluster restarts in Ignite can be very fast, as the data 
becomes operational instantaneously directly from disk. As a result, the data 
does not need to be preload [...]
+High-performance computing (HPC) is the ability to process data and perform 
complex calculations at high speeds. Using Apache Ignite as a [high-performance 
compute cluster](https://ignite.apache.org/use-cases/hpc.html), you can turn a 
group of commodity machines or a cloud environment into a distributed 
supercomputer of interconnected Ignite nodes. Ignite enables speed and scale by 
processing records in memory and reducing network utilization with APIs for 
data and compute-intensive calc [...]
 
-[Read More](https://apacheignite.readme.io/docs/clustering)
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