Fig 1 - GridGain Unified Real-time Data Platform at-a-glance
The GridGain Unified Real-Time Data Platform combines distributed processing and memory-centric storage to deliver low-latency analytics at scale. The commercial offering builds on the core Apache Ignite functionality, and is available in Community, Enterprise, and Ultimate Editions that provide successive additional features on top of Ignite. It is available for deployment on-premises, on any public cloud (including hybrid-, multi- and inter-cloud deployments) and through GridGain Nebula, either as a self-service SaaS on public cloud or as a fully managed service anywhere. An overview of the product is shown in Figure 1.
Gridgain’s core selling points are twofold. First, it provides a wide variety of unified data functionality. This includes handling data at rest and in motion, on-disk and in-memory storage, transactional and analytical processing, support for streaming and historical data, AI and MLOps, integration with (and thus access to) your system(s) of record, and more. This can dramatically simplify your data architecture, since it allows you to replace many point solutions with one unified platform, reducing the time, effort and money spent on maintaining both a laundry list of data products and the connections between them. Moreover, it will frequently be good for performance, as the majority of your data and data processing will happen over one tightly-integrated platform rather than a much looser collection of solutions that will often require data to be moved between them in order to process it.
This feeds into the second major draw for the GridGain platform, which is that it allows you to operate on your data with very low latency, even for the numerous (and often mixed) types of workloads we have just described. It primarily achieves this via in-memory computing, supported by the platform’s in-memory data grid. In fact, GridGain started life as an in-memory data grid, and as such these capabilities are some of its most highly developed.
This is particularly pertinent for addressing use cases that require real-time, large-scale data processing and decisioning, such as real-time AI, fraud detection, risk management, 360-degree view, and operational analytics. At the same time, in-memory compute alone will not always be enough to operate in real-time while addressing these use cases, as you will also need to contend with latency issues caused by data movement, the need to aggregate data that is held in separate silos, massive scale requirements, and highly complex workloads. As such, GridGain has been designed to address these problems.
For instance, as a unified platform it inherently helps eliminate silos and reduce the need for data movement. Its use of collocated analytics, performed wherever the relevant data resides, reduces the latter even further. We have already mentioned the platform’s ability to operate on a variety of workloads, and it is also highly scalable without compromising its performance.
In short, GridGain has developed into a unified, real-time platform for operating on data at scale and at speed that provides a wide array of data-oriented capabilities, including storage, processing, and analysis, in order to address the business needs surrounding data in a multi-dimensional fashion.
Customer Quotes
“By using Gridgain, we have been able to reduce our pricing strategy computations from many seconds to milliseconds. We have scaled out our computing platform across all geographies to centrally manage our pricing data and provide real-time promotions to our customers.”
EssilorLuxottica
“GridGain met our criteria for ACID compliance, support for high volume transactions, horizontal scalability, and co-located data processing.”
American Airlines
