The GridGain Systems In-Memory Computing Blog

Note: This is the third and final post in the blog series: Continuous Machine Learning at Scale With Apache Ignite. For post 1 click here and for post 2 click here. In my first post, I introduced Apache® Ignite™ machine learning and explained how it delivers large-scale, distributed, machine-learning (ML) workloads. In my second post, I discussed the Apache Ignite model-building stages. The…
Note: This is post 2 in the blog series: Continuous Machine Learning at Scale with Apache Ignite. For post 1 click here and for post 3 click here. In my first post, I introduced the topic “continuous machine learning at scale with Apache Ignite,” which is how we members of the Apache® Ignite™ community describe machine learning (ML) architectures that offer the following advantages: Support…
Note: This is post 1 in the blog series: Continuous Machine Learning at Scale with Apache Ignite. For post 2 click here and for post 3 click here. This is my first blog post in a series that discusses continuous machine learning at scale with the Apache® Ignite™ machine learning (ML) library. In this article, I’ll introduce the notion of continuous machine learning at scale. Then, I’ll discuss…
Kafka with Debezium and GridGain connectors allows synchronizing data between third party Databases and a GridGain cluster. This change data capture based synchronization can be done without any coding; all it requires is to prepare configuration files for each of the points. Developers and architects who can’t yet fully move from a legacy system can deploy this solution to give a performance…
Memory access is so much faster than disk I/O that many of us expect to gain striking performance advantages by merely deploying a distributed in-memory cluster and start reading data from it. However, sometimes we overlook the fact that a network interconnects cluster nodes with our applications, and it can quickly diminish the positive effects of having an in-memory cluster if a lot of data…
My acquaintanceship with PostgreSQL started back in 2009 - the time when many companies were trying to board the social networking train by following Facebook's footsteps. An employer I used to work for was not an exception. Our team was building a social networking platform for a specific audience and faced various architectural challenges. For instance, soon after launching the product and…
Introduction The Spark SQL engine provides structured streaming data processing. The benefit here is that users can implement scalable and fault-tolerant data stream processing between the initial data source and final data sync. You can read more about it here: https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html Apache Ignite provides the…
Ease-of-use is one of the core requirements at GridGain® which influences the way we see and build our products. While in-memory computing is a complex topic, the application development experience should not be equally complex. In the coming months you will see changes to GridGain and Apache® Ignite™ that will simplify Core APIs and the way that you debug running applications. In keeping with…
GridGain recently started publishing the Best Practices for Digital Transformation with In-Memory Computing (IMC) eBook series. The series captures some of the best practices for putting the right people, processes, and technology in place that helped early adopters succeed with their digital transformations. This blog post summarizes the first eBook in the series and outlines the best…
In a bid to speed the development and rollout of applications built on GridGain or Apache Ignite, GridGain Systems has just launched "GridGain Developer Bundles." These bundles include Support, Consulting and Training for GridGain Community or Enterprise Edition. The new Developer Bundles help companies implementing Apache® Ignite™ or GridGain speed the development and rollout of real-time,…