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IGNITE-14942 Fixes README description. (#65)
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ololo3000 committed Jun 25, 2021
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<a href="https://ignite.apache.org/"><img src="https://github.com/apache/ignite-website/blob/master/images/ignite_logo_full.svg" hspace="20"/></a>

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## What is Apache Ignite?
## What Are Apache Ignite Extensions?

Apache Ignite is a distributed database for high-performance computing with in-memory speed.
Apache Ignite Extensions is a group of integrations between [Apache Ignite](https://ignite.apache.org) and various Java frameworks.
To see the comprehensive list of Apache Ignite Extensions and related documentation, visit the *"Extensions and Integrations"* section of an [Apache Ignite Documentation](https://ignite.apache.org/docs/latest/).

<p align="center">
<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>
## Apache Ignite Extensions Publication And Versioning.

* [Technical Documentation](https://ignite.apache.org/docs/latest/)
* [JavaDoc](https://ignite.apache.org/releases/latest/javadoc/)
* [C#/.NET APIs](https://ignite.apache.org/releases/latest/dotnetdoc/api/)
* [C++ APIs](https://ignite.apache.org/releases/latest/cppdoc/)
* [Scala APIs](https://ignite.apache.org/releases/latest/scaladoc/scalar/index.html)
Each Apache Ignite extension is published as a separate Maven artifact and has an independent release lifecycle. Visit the *"Extensions and Integrations"* section of an [Apache Ignite Documentation](https://ignite.apache.org/docs/latest/) to see Maven Artifact ID and available versions for each extension, as well as the corresponding Apache Ignite and extension target compatible versions.

## Multi-Tier Storage

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.

[Read More](https://ignite.apache.org/arch/multi-tier-storage.html)

## Ignite Native Persistence

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://ignite.apache.org/features/transactions.html)

## 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.

[Read More](https://ignite.apache.org/features/sql.html)

## 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.

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 calculations. Those APIs implement the MapReduce paradigm and allow you to run arbitrary tasks across the cluster of nodes.

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