The GridGain Systems In-Memory Computing Blog
Hadoop Data Lakes are an excellent choice for analytics and reporting at scale. Hadoop scales horizontally and cost-effectively and performs long-running operations spanning big data sets. GridGain, in its turn, enables real-time analytics across operational and historical data silos by offloading Hadoop for those operations that need to be completed in a matter of seconds or milliseconds.
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Apache Ignite Deployment Patterns
The Apache Ignite® in-memory computing platform comprises high-performance distributed, multi-tiered storage and computing facilities, plus a comprehensive set of APIs, libraries, and frameworks for consumption and solution delivery (all with a “memory first” paradigm). This rich set of capabilities enables one to configure and deploy Ignite in many diverse…
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…
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…