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
Apache Ignite is a tool in the In-Memory Databases category of a tech stack.
Apache Ignite is an open source tool with 3K GitHub stars and 1.4K GitHub forks. Here’s a link to Apache Ignite's open source repository on GitHub
Who uses Apache Ignite?
6 companies reportedly use Apache Ignite in their tech stacks, including SEMrush, SaleCycle, and Tech.
11 developers on StackShare have stated that they use Apache Ignite.
Why developers like Apache Ignite?
Here’s a list of reasons why companies and developers use Apache Ignite
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Apache Ignite's Features
- Memory-Centric Storage
- Distributed SQL
- Distributed Key-Value
Apache Ignite Alternatives & Comparisons
What are some alternatives to Apache Ignite?
See all alternatives
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.
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.
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 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.
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.