The GridGain Systems In-Memory Computing Blog

Today, the glossy veneer of air travel is gone. Customers are increasingly impatient and intolerant and expect airlines to operate perfectly. This puts tremendous pressure on the airline industry to excel at operations and delight customers to overcompensate for the inconveniences associated with all the security and regulatory processes imposed on air travelers. The airline industry has been…
Some databases have a feature where a column can be “auto-incremented.” In this piece, we’ll explain why GridGain and Apache Ignite do not, and what you can do to get equivalent functionality. The naming varies, but the concept is straightforward: the system automatically generates a key for you if there is no unique business key for a table. Typically, this would be a numeric column, and the…
We hosted a rich technical conversation around the data challenges faced by Fintechs on our recent webinar, “Architecting Your Data Ecosystem for Real-Time Analytics: Best Practices for Fintechs.” This included many great questions from our audience during the Q&A. Here is a recap of the Q&A, including questions we weren’t able to get to during the live event. What is driving the need for…
This eight-minute GridGain University Micro Learning Unit explores the importance of colocation and affinity to the performance of Apache Ignite data query and computation.  The two largest sources of latency in any distributed system are network latency and disk access. In traditional client server applications, data is constantly moved over the network, and it's usually accessed from disk.…
In today's fast-paced business environment, it’s crucial to gain a competitive edge by quickly extracting meaningful insights from diverse datasets. It is also vitally important to be responsive to changing data as soon as possible. These requirements demand a data solution that is flexible and performant. JSON (JavaScript Object Notation) data format, which supports unstructured or semi-…
Welcome to part two of our blog series on “Understanding Then Optimizing GridGain Query Processing.” To properly understand this second blog, it’s highly recommended that you familiarize yourself with the background material that was shared in the first blog post of this two-part series.  With the requisite background in place, we can now explore how to overcome the limitations of standard…
Introduction In this two-part blog post, we will first explore and understand the major steps that are executed by GridGain’s SQL query processing engine. More importantly, we will share how data is exchanged in the above process and learn about certain limitations that exist within this process.  After learning the basics in part one, we will then explore how to overcome those limitations in…
This six-minute Micro Learning Unit explores how distributed data is implemented in Apache Ignite and identifies solutions to three key data challenges: hardware capacity, hardware reliability, and performance issues. Capacity: It’s more efficient to scale data capacity horizontally than vertically. For example, extremely fast CPUs typically cost several times as much as two slightly slower…
In a previous article, I discussed redefining the challenge facing companies that want to become data-driven. The way most people think about this problem – and the most commonly proposed solution – is putting all data into a single place, such as a data lake. This strategy has challenges, the biggest of which is that while data lakes make it economical to store data, retrieval, and analysis…
Focusing on how Apache Ignite handles transactions with third-party persistence, this article is the last part of the Apache Ignite Transactions Architecture series. In the previous articles in this series, we discussed a range of topics associated with Apache Ignite's transactions handling in its Key-Value API. In the first article, we briefly reviewed the two-phase commit protocol and…