GridGain In-Memory Data Fabric vs Hazelcast

Compare features, pricing, and capabilities to find which solution is best for your needs.

GridGain In-Memory Data Fabric

GridGain In-Memory Data Fabric is a comprehensive in-memory computing platform designed for high-performance, low-latency applications. It acts as a data and compute layer between applications and traditional databases, accelerating access and processing of vast amounts of data for real-time analytics, transactional workloads, and streaming data. by GridGain Systems, Inc

Open Source

Platforms: Windows Linux

Screenshots:

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Hazelcast

Hazelcast is a leading open-source distributed in-memory data grid and stream processing platform designed for high-performance, low-latency applications. It provides incredibly fast data access and processing by keeping data in RAM across a cluster of machines, enabling real-time responsiveness. by Hazelcast, Inc.

Open Source

Platforms: Mac OS X Windows Linux

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Comparison Summary

GridGain In-Memory Data Fabric and Hazelcast are both powerful solutions in their space. GridGain In-Memory Data Fabric offers gridgain in-memory data fabric is a comprehensive in-memory computing platform designed for high-performance, low-latency applications. it acts as a data and compute layer between applications and traditional databases, accelerating access and processing of vast amounts of data for real-time analytics, transactional workloads, and streaming data., while Hazelcast provides hazelcast is a leading open-source distributed in-memory data grid and stream processing platform designed for high-performance, low-latency applications. it provides incredibly fast data access and processing by keeping data in ram across a cluster of machines, enabling real-time responsiveness.. Compare their features and pricing to find the best match for your needs.

Pros & Cons Comparison

GridGain In-Memory Data Fabric

Analysis & Comparison

Advantages

Extremely low data access latency due to in-memory architecture.

High scalability and availability through distributed clustering.

Provides both SQL and key-value interfaces for data access.

Integrated distributed computing engine reduces data movement.

Supports ACID transactions for data consistency.

Good integration capabilities with external data sources.

Limitations

Requires significant RAM resources.

Operational complexity inherent in managing a distributed system.

Potential for data loss if not configured for persistence and system crashes occur.

Can have a steeper learning curve compared to traditional databases for some users.

Hazelcast

Analysis & Comparison

Advantages

Extremely low latency due to in-memory data storage.

Highly scalable and fault-tolerant architecture automatically handles data distribution and replication.

Integrated stream processing engine for real-time analytics and event processing.

Offers familiar distributed data structures simplifying development.

Pure Java implementation integrates well with Java ecosystems.

Flexible deployment options including cloud and containers.

Limitations

In-memory nature means data size is limited by available RAM.

Requires operational knowledge to effectively manage and tune large clusters.

Steeper learning curve initially compared to simpler caching solutions.

Persistence requires additional configuration and can introduce complexity.