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.
GridGain In-Memory Data Fabric image
inmemory database data-grid distributed-computing

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.

What is GridGain In-Memory Data Fabric?

GridGain In-Memory Data Fabric is a distributed in-memory computing platform that enables organizations to develop data-intensive applications that require high performance and massive scalability. It provides an in-memory data grid that can be accessed by applications, allowing them to store and process data with in-memory speeds.

Some key capabilities and benefits of GridGain include:

  • Very fast in-memory access for both transactions and analytics resulting in orders of magnitude performance boost over disk-based solutions.
  • Support for hybrid transactional/analytical processing (HTAP) allowing transactional and analytical workloads to run simultaneously on the same in-memory dataset without impacting performance.
  • Distributed architecture that provides linear scalability by allowing to scale out across commodity servers as data and processing needs grow.
  • ANSI-99 SQL support including ACID transactions, JDBC drivers and automatic data partitioning across cluster for querying very large datasets.
  • MapReduce implementation and support for programming languages like Java, .NET, C++ for developing massively parallel analytical applications.
  • Event streaming and continuous SQL capabilities for streaming analytics and real-time predictive modelling.
  • Support for Kubernetes, Docker and OpenStack deployments as well as cloud platforms like AWS and Azure.

Overall, GridGain provides organizations with a high performance, scalable in-memory data platform for their critical transactional, analytical and streaming data workloads.

GridGain In-Memory Data Fabric Features

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


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