Infinispan vs GridGain In-Memory Data Fabric

Struggling to choose between Infinispan and GridGain In-Memory Data Fabric? Both products offer unique advantages, making it a tough decision.

Infinispan is a Development solution with tags like caching, distributed-cache, inmemory-data-grid.

It boasts features such as Distributed in-memory key/value data store, Supports transactions, Data partitioning, Data replication, Persistence to disk or database, Querying and indexing, Caching for databases, Clustering and high availability and pros including Very fast data access, Scalable and elastic, Fault tolerant, Supports many data structures, Integrates with many frameworks/platforms, Open source with large community.

On the other hand, GridGain In-Memory Data Fabric is a Development product tagged with inmemory, database, data-grid, distributed-computing.

Its standout features include In-memory data storage and processing, Distributed caching, In-memory SQL queries, In-memory compute grid, High availability through data replication, Horizontal scalability, ACID transactions, ANSI SQL support, Streaming and CEP, Machine learning and predictive analytics, and it shines with pros like Very fast performance for data-intensive workloads, Low latency for real-time applications, Scales horizontally, Supports both SQL and key-value APIs, Open source and commercially supported options available.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Infinispan

Infinispan

Infinispan is an open source in-memory data grid and distributed cache. It is used to store and retrieve data with microsecond response times, while providing data reliability and availability.

Categories:
Development Caching & Performance
caching distributed-cache inmemory-data-grid

Infinispan Features

  1. Distributed in-memory key/value data store
  2. Supports transactions
  3. Data partitioning
  4. Data replication
  5. Persistence to disk or database
  6. Querying and indexing
  7. Caching for databases
  8. Clustering and high availability

Pricing

  • Open Source

Pros

Very fast data access

Scalable and elastic

Fault tolerant

Supports many data structures

Integrates with many frameworks/platforms

Open source with large community

Cons

Complex to configure and operate

Not ideal for durable long-term storage

Can be resource intensive

Not as fully featured as some proprietary options

GridGain In-Memory Data Fabric

GridGain In-Memory Data Fabric

GridGain In-Memory Data Fabric is an in-memory computing platform that provides in-memory speed and massive scalability for data-intensive applications. It allows organizations to process transactions and analyze data in real-time.

Categories:
Development In-Memory Databases
inmemory database data-grid distributed-computing

GridGain In-Memory Data Fabric Features

  1. In-memory data storage and processing
  2. Distributed caching
  3. In-memory SQL queries
  4. In-memory compute grid
  5. High availability through data replication
  6. Horizontal scalability
  7. ACID transactions
  8. ANSI SQL support
  9. Streaming and CEP
  10. Machine learning and predictive analytics

Pricing

  • Open Source
  • Freemium
  • Subscription-Based

Pros

Very fast performance for data-intensive workloads

Low latency for real-time applications

Scales horizontally

Supports both SQL and key-value APIs

Open source and commercially supported options available

Cons

Can require large amounts of RAM to store data in-memory

Not ideal for storing large amounts of infrequently accessed data

Complexity of distributed system configuration and management