The GridGain Systems In-Memory Computing Blog
Businesses today are increasingly complex leading to slow performance which negatively impacts customer experiences, productivity, and ultimately revenue. An in-memory data fabric addresses data complexity head-on.
Companies today are facing a significant problem – their reliance on disk-based data stores are slowing down performance and costing them valuable time and money. Enter GridGain’s…
The Apache Ignite native persistence storage engine follows a classic database approach based on the ARIES architecture. However, the Ignite developers needed to make some adjustments to the architecture in order to improve development speed and support memory-only storage.
In this blog, I will provide an overview of the Ignite native persistence storage engine and discuss the tradeoffs that…
The Apache Ignite community is maintained by dedicated volunteers who manage a highly informative and well-designed website. This website is regularly updated with the latest news and information about the open-source project. A section of this content, found on the GridGain blog, provides an overview of Apache Ignite's basic concepts, facts, tips, and tricks. The goal of this blog post is to…
After five days (and eleven meetings) with new customers in Europe and the Middle East, I think the time is right for another refinement of in-memory computing’s definition. To me, it is clear that our industry is lagging when it comes to explaining in-memory computing to potential customers and defining what in-memory computing is really about. We struggle to come up with a simple,…
Where do you store your passwords?
Whether you’re integrating Apache Ignite with a relational database, a message queue, or something else, you probably need to manage secrets such as usernames, passwords, and security tokens. In this post, we consider a couple of options to avoid having secrets in your configuration file: using property files and integrating with HashiCorp Vault.…
Apache® Ignite™ is a very versatile product that supports a wide-range of integrated components. These components include a Machine Learning (ML) library that supports popular ML algorithms, such as Linear Regression, k-NN Classification, and K-Means Clustering.
The ML capabilities of Ignite provide a wide-range of benefits, as shown in Figure 1. For example, Ignite can work on the data in place…
For those forced to read Plato’s Republic, you may remember the allegory of the cave, where people are chained to the wall. They can only see shadows of figures and hear voices that echo off the walls. This copy becomes their reality. In reading through the GridGain® and Redis® feature comparison, it’s easy to get lost in the 13 pages of…
Held on November 9, Ignite Summit Europe 2022 provided a closeup look into how Apache Ignite is being adapted for use in a variety of dynamic, demanding business settings and use cases. (Video of all sessions is available on-demand here.) Presenters were from organizations as diverse as a financial software tools, the largest food delivery company in Latin America, and the largest particle…
One of the most heavily used features in GridGain Nebula is the flexible dashboarding capabilities for monitoring cluster and node status. The drag-and-drop interface enables access to more than 200 metrics that are available in Apache Ignite or on the GridGain In-memory Computing Platform software running in data centers, private clouds, or the Nebula cloud.
Until now, there was no guidebook…
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…