The GridGain In-Memory Data Fabric, built on Apache Ignite, is an in-memory computing platform which offers a variety of in-memory computing solutions that allow new or existing disk-based applications to run queries 1,000x or more faster and scale out to support petabytes of in-memory data. The GridGain Enterprise Edition builds on Apache Ignite by including additional features and undergoing additional QA testing which makes it easier to deploy, manage and secure in production environments. The GridGain Enterprise Edition is deployed as an in-memory computing layer between the application and data layers. GridGain includes powerful features for in-memory computing including an in-memory data grid, in-memory compute grid, in-memory SQL grid, in-memory streaming engine, and much more.
HTAP use cases.
The GridGain Enterprise Edition includes the following features in addition to the core features of Apache Ignite.
Management & Monitoring GUI
Our GUI-based Management and Monitoring tool provides a unified operations, management and monitoring system for GridGain deployments. Visor provides a deep management and monitoring view into all aspects of GridGain operations – from HPC and Data Grid to Streaming, and Hadoop acceleration, via standard dashboards, advanced charting of performance metrics, and grid health (telemetry) views, among many other features.
The GridGain Enterprise Edition Enterprise-grade Security provides extensible and customizable authentication and security capabilities. It includes both a Grid Authentication SPI and a Grid Secure Session SPI to satisfy a variety of security requirements.
Network Segmentation Protection
Network Segmentation Protection detects detect any network disruption within the grid to prevent transactional data grids from developing a ‘split brain’ scenario. The options for handling these network occurrences are fully configurable to handle these situations the way you need to for a variety of use cases.
Recoverable Local Store
For large datasets, the loading of data into memory from slower relational data stores and appliances can take time which may take too long in some disaster recovery scenarios. The Recoverable Local Store feature is an optimized fast restart for data stored in-memory, via a local disk store. By preserving this data store locally, we remove any network impact as well. During normal operations, the GridGain Enterprise Edition already maintains multiple copies of data in memory to prevent loss due to node failures. This capability additionally takes care of widespread outages and speeds recovery during planned maintenance operations.
Rolling Production Updates
The Rolling Production Updates feature enables you to co-deploy multiple versions of our software and allow them to co-exist as you roll out new versions. This prevents any downtime when performing software upgrades.
Data Center Replication
GridGain reliably replicates data on a per-cache basis across two or more regions connected by wide area networks. This allows geographically remote data centers to maintain consistent views of data. With GridGain reliability and predictability, Data Center Replication ensures business continuity and can be used as part of a disaster recovery plan. Data Center Replication integrates with your application so that caches marked for replication are automatically synchronized across the WAN link. Predictable performance includes:
- Asynchronous data replication to maximize performance
- Fault tolerance
- Conflict resolution when needed
- Scaling (at the local cluster, and in the number of WAN-linked clusters)
- Multiple types of topologies supported
- Brokerless (no JMS broker needed for replication)
Oracle® GoldenGate Integration
The Oracle GoldenGate integration in the GridGain Enterprise Edition provides real-time data integration and replication into a GridGain cluster from different environments. When users configure GoldenGate integration replication, the GridGain in-memory computing platform will automatically receive updates from the connected source database, converting the data from a database relational model to cache objects.