GridGain vs GigaSpaces: In-Memory Data Grid Feature Comparison
See how GridGain In-Memory Data Fabric (built on Apache Ignite) compares to GigaSpaces across SQL, transactions, off-heap memory, streaming, integrations, and enterprise operations.
- Compare ANSI-99 SQL, distributed joins, and JDBC/ODBC support
- Review deadlock-free optimistic transactions and cross-partition transactions
- Evaluate off-heap memory options and off-heap index support
- See in-memory streaming and sliding window capabilities side-by-side
- Compare caching standards like JCache (JSR-107) implementations
- Assess integrations, cloud support, management, and security capabilities
About this feature comparison
This document provides a feature comparison of GridGain In-Memory Data Fabric and GigaSpaces for in-memory computing applications. It outlines major differences in how each product handles caching, transactions, and querying in distributed clusters.
GridGain (built on Apache Ignite) is positioned as an in-memory computing platform that can be inserted between application and database layers to accelerate data-intensive systems, with support for integrating with RDBMS, NoSQL, and Hadoop and running on-prem, hybrid, or cloud environments (including AWS and Azure).
Key differences highlighted include: JCache (JSR-107) implementation approach, memory formats (on-heap/off-heap) and off-heap indexes, SQL support (ANSI-99) with distributed joins, deadlock-free optimistic transactions, cross-partition transactions, and in-memory streaming with sliding windows (noted as not supported by GigaSpaces).
GridGain supports complete SQL (ANSI-99) syntax, including distributed SQL JOINs… GigaSpaces does not support SQL and users have to perform JOINs manually.
GridGain vs GigaSpaces: In-Memory Data Grid Feature Comparison
Compare GridGain vs GigaSpaces across ANSI-99 SQL, distributed joins, deadlock-free transactions, cross-partition transactions, off-heap options, and in-memory streaming.
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