GridGain Unified Real-Time Data Platform

We have been working with GridGain for about a year to help us continuously train our AI and ML models. Their Continuous Learning Framework supports machine learning and deep learning model training on operational data, with built-in machine learning libraries that facilitate training models using real-time data. GridGain also provides us with high-speed data processing and analytics, and near-linear scalability which have been key to helping us handle large datasets and perform the complex computations required in AI and ML training.

Gridgains supports SQL queries directly on cached data. Supports ACID transactions.

With the Ultimate Edition, we have an enterprise solution that is very fast, stable, scalable and supports data recovery capabilities. Over time we'd had many productive calls with the support team and found them very knowledgeable and helpful, any issues we've found generally get worked into a future release (or patch if needed).

We've experienced minor issues with some of our data integrations when it came to scaling our AI and ML models.

Complex to set up and manage. You need expertise in in-memory computing systems.

Change data capture isn't as easy to do as a database trigger as the Continuous Query is separate from the data and runs as a service, so it is a little more complex and requires extra logic if the service is down while data is still being updated. But there are ways to make it work.