GridGain and Pivotal GemFire Feature Comparison


Pivotal GemFire® is an in-memory data grid (IMDG) powered by the Apache Geode open source project. It is used by companies to scale data services on demand to support high-performance, real-time applications.

GridGain®, built on the Apache® Ignite open source project, is an in-memory computing platform that includes a distributed in-memory data grid (IMDG), a hybrid SQL and key-value in-memory database (IMDB), a stream processing and analytics engine, and a continuous learning framework that supports real-time machine and deep learning. It can be used with any RDBMS, NoSQL or Hadoop database.

GemFire has many core IMDG capabilities, including the ability to distribute and partition data, and scale out across a cluster. But GemFire hasn’t evolved much in the last few years. Gemstone, the original company, was acquired in 2010 by VMWare and later spun out as part of Pivotal in 2012. In 2015 Pivotal donated GemFire code to the Apache Software Foundation for the Apache Geode project. Geode became a top-level Apache project the end of 2016.

GridGain is a superior IMDG for the majority of existing applications. This is due in part to being built on the innovative Apache Ignite project. GridGain Systems donated the original Apache Ignite code to the Apache® Software Foundation (ASF) in 2014 and remains the most active contributor. Ignite became a top level ASF project in 2015. Ignite is now one of the top five Apache Software Foundation open source projects in commits and list activity, with over twice the commits of Apache Geode.

Major Advantages of GridGain vs. GemFire

  • Comprehensive in-memory computing platform
  • Native ANSI-99 SQL support
  • Distributed ACID transaction support with immediate consistency using built-in persistence
  • Sits in-between SQL-based applications and RDBMSs, eliminating the need to replace SQL with code
  • Native integration with RDBMSs, NoSQL databases and Hadoop
  • Distributed in-memory database with better transaction support, scalability, availability and reliability
  • Built-in Continuous Learning Framework for machine learning and deep learning
  • Support for Apache Spark™ DataFrames, RDDs, HDFS and SparkSQL acceleration
  • Built on Apache Ignite, a top 5 Apache Software Foundation project

Detailed GridGain and GemFire Feature Comparison

This in-depth feature comparison shows how the most current versions of GridGain Professional Edition, Enterprise Edition, Ultimate Edition and GemFire compare in 25 different categories including:

  • Use Cases
  • 3rd Party Database Support and Persistence
  • Support for Native Persistence
  • Distributed SQL and Queries
  • ACID Compliant Transactions and Locks
  • Memory Architecture and Optimization
  • Distributed Architecture
  • Data Rebalancing
  • Distributed Computing
  • In-Memory Streaming and Integration with Apache Spark
  • Security and Audit
  • Configuration and Grid Management
  • Supported Platforms, Standards and Out-of-the-Box Integration
  • Cloud and Virtualization Support

Feature GridGain CE 8.7
Apache Ignite 2.7
GridGain EE 8.7 GridGain UE 8.7 GemFire 9.7
In-Memory Data Grid
Third Party Database Caching and Persistance (inline)
SQL Database
(+ Multi-datacenter data and disaster recovery management)
In-Memory Database
(+ Multi-datacenter data and disaster recovery management)
Web Session Clustering
Apache Spark Acceleration
(Spark RDD Support provided through Spark Connector. Also allows OQL to return DataFrame. SnappyData also combines GemFire and Spark)
Hadoop Acceleration  
In-Memory File System (Hadoop Compliant)  
Third Party Database Support, Persistence
Automatic Support for Leading RDBMSs (Oracle, IBM DB2, Microsoft SQL Server, MySQL, Postgres, ... ) (It works well, but requires coding to implement, it's not out of the box)
Automatic Integration with Apache Cassandra  
Inline Support for MongoDB  
Write Through and Read Through
Write-Behind Caching
Auto-Loading of SQL Schema/Data