Apache Ignite In-Memory Computing Platform

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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.

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In-Memory Cache

Use Ignite as a low-latency and high-performance in-memory cache with Key-Value and ANSI SQL support.

In-Memory Data Grid

Gain 100x acceleration by using Ignite as an advanced in-memory data grid on top of RDBMS, Hadoop or another store.

In-Memory Database

Scale out and up across RAM, NVRAM, Flash, and disk with Ignite as an in-memory database with distributed multi-tier storage.

Apache Ignite Use Cases

Applications Acceleration & Data Caching

Gain up to 100x acceleration for existing services, applications, and APIs using Apache Ignite as a distributed high-performance in-memory layer. Keep underlying databases in sync, using Ignite as an in-memory data grid.

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Applications Acceleration & Data Caching diagram
Digital Integration Hub diagram

Digital Integration Hub

Consolidate multiple internal systems and external data silos with Apache Ignite as a low-latency, high-performance shared data store. Use Apache Ignite to cache and persist data sets dispersed across multiple disjointed external databases and systems providing developers with functionally rich, unified and consistent APIs.

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Apache Ignite Features

Multi-Tier Storage

Use memory, disk or Intel® Optane™ as active storage tiers with no need for caching of all the data and memory warm-ups.

Distributed SQL

Query both in-memory and disk-only data sets with ANSI SQL that supports distributed joins, DML, and DDL.

Distributed Key-Value

Read, write, and transact with high-performant key-value APIs that support JCache specification.

ACID Transactions

Execute distributed ACID transactions across memory and disk tiers, including relational databases.

Co-located Processing

Eliminate data shuffling by running data-intensive and compute-intensive calculations on cluster nodes.

Machine Learning

Train and deploy distributed machine learning models continuously over a horizontally scalable in-memory cluster.

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