GridGain Benefits and Insights

Why use GridGain?

Key differentiators & advantages of GridGain

Extensive Scalability: Distributed across a cluster of servers, it scales up data-intensive apps to hold petabytes of data without sacrificing performance through data rebalancing, automated node addition, data locality and affinity routing.
Improve Performance: Leverage its distributed in-memory data grid and in-memory database to eliminate inherent delays associated with disk-centric data access and handle high-performance app requirements. Perform a plethora of events every second on moderate clusters and processes simultaneously using massively parallel processing (MPP).
Preventative Troubleshooting: Monitor extensively to proactively manage bottlenecks and manage spikes. Optimize unbalanced nodes and distribution through heatmaps, visualizations and alerts.
Reduce Data Noise: Programmed to eliminate unnecessary data noise by maintaining data locality and affinity data-routing.
Ensure Disk Durability: Replicate only the most time-intensive datasets in memory and keep the original database intact. Scale and tweak apps without changing the original database. Start processing even before loading data into memory by ensuring complete operability from disk.
Lower Cost and Complexity: Standardize on a single memory-centric platform to reduce implementation times and maintenance expenses.
Leverage Streaming Data: Integrates with streaming modules like Apache Spark to pull, process and publish streaming data faster for large-scale apps. Process millions of events in seconds with distributed clusters and MPP libraries to enable real-time streaming.
Continuous App Availability: Introduce updates without downtimes and interruptions, allowing data to coexist globally throughout disparate data servers. Prevent downtimes during regional power interruptions by balancing workloads.
Security and Compliance: Keep data secure and adhere to compliance standards with audits, node and communication encryptions, multi-tenancy, customizable data authentication, and management of roles and permissions.
Prevent Data Loss: Schedule data backups and restore an earlier stable state using continuous or manual archives.
Perform HTAP: Reduce or eliminate the separation of transactional and analytical processing — run both processes faster on a single distributed in-memory infrastructure.

Industry Expertise

The vendor serves all industries adapting to a digital model, including financial, logistics, software, healthcare, retail, telecommunications and more.

Key Features

In-Memory Data Grid: Deploys a data grid between the application and data layers to eliminate delays in utilizing disk-based data, and improve scalability and performance. Supports off-heap storage, load-balancing and fault tolerance.
In-Memory Database: Includes a scalable memory-centric database that can handle high-performance apps, ACID transactions and ANSI-99 SQL. Execute distributed SQL, key value and other processes throughout layers. Configure and optimize the balance between infrastructural expenses and performance.
Distributed Computing: Accelerate performance by distributing datasets throughout a cluster of servers with automated load balancing. Distribute in-memory cache, transactions, SQL queries and joins, queue and other data structures across multiple nodes to enable massively parallel processing.
Continuous Learning Framework: Process and train machine learning and deep learning models faster using its MPP libraries directly in the cluster, without separate ETL requirements. Train models continuously and in real time.
Transactional Persistence: Decide which parts of the distributed dataset to keep in memory. Ensure transactional persistence by retaining data and SQL indexes on disk.
Data Monitoring and Visualization: Track processes to identify bottlenecks, uneven distribution, unbalanced nodes and root causes of spikes. Visualize resource usage, spikes, updates and performance metrics.
Integrations: Integrates seamlessly with relational operational databases (RDBMS), NoSQL databases and data lakes. Popular integrations include Apache Hadoop, Apache Cassandra, Apache Kafka, Apache Camel, Apache Mesos, Memcached, Redis, and Hadoop Yarn.

Limitations

At the time of this review, these are the limitations according to user feedback:

Programming may become lengthy.
Difficult to implement.
Has a steep learning curve; difficult for non-coders.

Suite Support

Email: Not available.

Phone: Not available.

Training: Navigate to the “learn” dropdown at the top of the vendor website to register for instructor-led training courses and access extensive resources such as live and on-demand webinars, blogs, YouTube videos and community discussions.

Tickets: Registered customers can log in and submit tickets through the vendor support portal.

GridGain Reviews & Pricing

What is GridGain?

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