Apache Ignite
Apache Ignite

35
53
+ 1
14
Redis
Redis

24.8K
18.9K
+ 1
3.8K
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Apache Ignite vs Redis: What are the differences?

Developers describe Apache Ignite as "An open-source distributed database, caching and processing platform *". It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale. On the other hand, *Redis** is detailed as "An in-memory database that persists on disk". Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.

Apache Ignite and Redis belong to "In-Memory Databases" category of the tech stack.

Apache Ignite and Redis are both open source tools. It seems that Redis with 37.9K GitHub stars and 14.6K forks on GitHub has more adoption than Apache Ignite with 2.67K GitHub stars and 1.3K GitHub forks.

According to the StackShare community, Redis has a broader approval, being mentioned in 4259 company stacks & 8884 developers stacks; compared to Apache Ignite, which is listed in 4 company stacks and 4 developer stacks.

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    What is Apache Ignite?

    It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

    What is Redis?

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
    What companies use Apache Ignite?
    What companies use Redis?

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    What tools integrate with Apache Ignite?
    What tools integrate with Redis?

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    What are some alternatives to Apache Ignite and Redis?
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    Hazelcast
    With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    See all alternatives
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