GridGain vs Pivotal GemFire: In-Memory Data Grid Feature Comparison

Compare GridGain (Apache Ignite–based) and Pivotal GemFire (Apache Geode–based) across SQL, distributed transactions, integrations, persistence, Spark support, and ML capabilities.

  • Compare ANSI-99 SQL support, drivers, and distributed JOINs
  • Evaluate distributed ACID transactions and isolation levels at scale
  • See “slide-in” architecture benefits for SQL-based applications
  • Review out-of-the-box integrations for RDBMS, NoSQL, and Hadoop
  • Compare persistence, tiered storage, and instantaneous restart behavior
  • Assess Spark DataFrame/RDD/HDFS support and built-in machine learning

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About this feature comparison

This document provides a summary and detailed comparison of the GridGain in-memory computing platform and Pivotal GemFire for use as in-memory data grids (IMDG) and broader in-memory computing use cases. It also explains how GemFire relates to Apache Geode, and how GridGain is built on Apache Ignite.

It highlights major technical differences that impact real-world adoption, including GridGain’s ANSI-99 SQL support (with JDBC/ODBC drivers and distributed JOINs) and distributed ACID transactions across nodes and partitions, versus GemFire’s lack of SQL support and limitations around distributed transactions beyond a single node.

You’ll also see capability-by-capability comparisons across integration (RDBMS/NoSQL/Hadoop), persistence and tiered storage, cluster operations, standards support, streaming, security/audit, and tooling—plus a clear breakdown of which features map to GridGain Community, Enterprise, and Ultimate editions versus GemFire.

GridGain supports ANSI-99 compliant SQL, including distributed SQL JOINs… GemFire does not support SQL.

GridGain vs Pivotal GemFire: In-Memory Data Grid Feature Comparison

Compare GridGain vs Pivotal GemFire across ANSI-99 SQL, distributed JOINs, distributed ACID transactions, integrations, persistence, Spark support, and machine learning.

Get the full comparison