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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…
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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…
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Data Distribution in Apache Ignite

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…
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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.…
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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…
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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…
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Now, in-memory cache technology is becoming popular, motivating companies to experiment with distributed systems. The technology is advertised to be fast, and data-load speed is often critical for building a successful solution prototype. This blog post provides a technical tutorial on how to populate a distributed Apache Ignite cluster with values that originate from large relational tables. All…
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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…
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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…
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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…
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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…
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In this second article of the three-part “Getting Started with Ignite Data Loading” series, we continue our review of data loading into Ignite tables and caches. However, we now focus on Ignite CacheStore. CacheStore Load Facility Background Let’s review what was discussed about CacheStore in “Article 1: Loading Facilities.” The CacheStore interface of Ignite is the primary vehicle used in…
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With this first part of “Getting Started with Ignite Data Loading” series we will review facilities available to developers, analysts and administrators for data loading with Apache Ignite. The subsequent two parts will walk through the two core Apache Ignite data loading techniques, the CacheStore and the Ignite Data Streamer. We are going to review these facilities in relation to specific…
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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. In…
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Apache Ignite can scale horizontally to accommodate the data that your applications and services generate. If your in-memory cluster is about to run out of memory space, you can take advantage of horizontal scaling, which is one of Ignite’s foundational architectural capabilities. The “throw more resources into the cluster” approach is an often-heard piece of advice. However, in practice, most of…
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