Since our initial launch in mid-2020, GridGain Control Center has strived to bring transparency and flexibility to the monitoring and development of Apache Ignite and GridGain applications. With each monthly update, we introduce new features to make it easier for admins and developers to understand what exactly is happening within their clusters. In our latest update (2020.11.00), we add new…
The GridGain Systems In-Memory Computing Blog
Telecommunication companies can transform their operations into data-driven enterprises by utilizing the Digital Integration Hub Architecture, which is built on GridGain's in-memory computing platform. In this blog post, we will explore how the Digital Integration Hub architecture can assist telecommunication companies in enhancing customer insights, creating additional revenue streams, and…
Why is it necessary to distribute data?
As systems that require data storage and processing evolve, they often reach a point where either the amount of data exceeds the storage capacity, or the workload surpasses the capabilities of a single server.
In such situations, there are two useful data distribution solutions: data sharding and migrating to a distributed database. Both…
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…
Imagine that we need to build a monitoring infrastructure for a distributed database, such as Apache Ignite. Let’s put metrics into Prometheus. And, let’s draw charts in Grafana. Let’s not forget about the notification system—we’ll set up Zabbix for that. Let’s use Jaeger for traces analysis. For state management, the CLI will do. As for SQL queries, let’s use a free JDBC client, such as DBeaver…
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…
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.…
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…