The GridGain Systems In-Memory Computing Blog
Featured Post
Just Released: Apache Ignite 3, Alpha 2
(Be sure to sign up for the upcoming virtual Ignite Community event on July 20, 2021, at 9:00 a.m. PST. Val will share a summary of changes planned in Apache Ignite 3 at this event. Community members will have an opportunity to share their feedback and discuss the proposed changes.)
Apache Ignite 3 is an ongoing, active project that focuses on creating the next generation of the Apache Ignite database. The goal is to retain the power and flexibility of the Ignite 2 platform, modernize the architecture, and significantly improve usability.
Previous Entries
We recently announced the GridGain and Apache Ignite Operator for Kubernetes, which gives GridGain and Apache Ignite users a convenient way to deploy and manage their clusters. The automation provided by the solution simplifies cluster provisioning and minimizes the operational and management burden. In addition, our latest updates to the GridGain thin client and thick client deliver simplified…
This blog is an abridged version of the talk that I gave at the Apache Ignite community meetup. You can download the slides that I presented at the meetup here. In the talk, I explain how data in Apache Ignite is distributed.
Why do you need to distribute anything at all?
Inevitably, the evolution of a system that requires data storage and processing reaches a threshold. Either too much data is…
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.…
Telcos can become a highly data-driven enterprise by leveraging the Digital Integration Hub (DIH) Architecture built on GridGain’s in-memory computing platform. In this blog post, I will discuss how the DIH architecture can help telcos develop better customer insights, generate new revenue streams and be ready to ride the 5G wave. This easy-to-adopt, no rip-replace architecture can meet the needs…
This tutorial walks you through the process of creating a Spring Cloud-based RESTful web service that uses Apache Ignite as a high-performance, in-memory database. The service is a containerized application that uses HashiCorp Consul for service discovery and interacts with an Apache Ignite cluster via Spring Data repository abstraction. For containerization, we use ? Docker.
Apache® Ignite™ is…
Publisher's Note: the article describes a custom data loading technique that worked best for a specific user scenario. It's neither a best practice nor a generic approach for data loading in Ignite. Explore standard loading techniques first, such as IgniteDataStreamer or CacheStore.loadCache, which can also be optimized for loading large data sets.
Now, in-memory cache technology is becoming…
Using the initial-query, listener, and remote-filter features of Ignite continuous queries to detect, filter, process, and dispatch real-time events
(Note that this is Part 3 of a three-part series on Event Stream Processing. Here are the links for Part 1 and Part 2.)
Real-time handling of streams of business events is a critical part of modern information-management systems, including online…
Building an Event Stream Processing Solution With Apache Ignite
(Note that this is Part 2 of a three-part series on Event Stream Processing. Here are the links for Part 1 and Part 3.)
In the first article of this three part series, we talked about streaming systems, the associated event paradigm inherent in streams and how these concepts are seen at different levels of abstraction, the…
Characteristics, Types & Components of an Event Stream Processing System
(Note that this is Part 1 of a three-part series on Event Stream Processing. Here are the links for Part 2 and Part 3.)
Like many technology-related concepts, Streams or “Event Streaming” is understood in many different contexts and in many different ways such that expectations for Event Stream Processing (ESP) vary…
In this third article of the three-part series “Getting Started with Ignite Data Loading,” we continue to review data loading into Ignite tables and caches, but now we focus on using the Ignite Data Streamer facility to load data in large volume and with highest speed.
Apache Ignite Data-Loading Facilities
In the first article of this series, we discussed the facilities that are available to…