Apache Ignite

Designing a distributed database that scales, stays consistent, and performs under pressure isn’t trivial. In Part 7 of the Apache Ignite 3 Architecture series, we take a deep dive into how Ignite 3 approaches core architectural challenges, from data distribution and replication to consistency and fault tolerance, and why these decisions matter for real-time workloads. For teams building systems that need fast transactions, real-time analytics, and predictable performance at scale, understanding these architectural foundations is key. 👉 Read the blog to learn how Apache Ignite 3 is engineered for modern, high-performance data platforms. 🔗 https://lnkd.in/eXH33-NH #RealTimeData #DistributedSystems #ApacheIgnite

Your transaction processing should enable operational intelligence, not constrain it. When applications outgrow single databases, they typically sacrifice either consistency or concurrency. Ignite 3's RAFT-backed MVCC preserves both. Read my blog post: MVCC Transactions for High-Frequency Processing to see how Ignite 3 handles the challenge. https://lnkd.in/gYzHvMd6 #ApacheIgnite #DistributedSystems #DistributedComputing #InMemoryComputing

It’s not too late to sign up! Join GridGain Sales Engineer, Nelzir Louiseize, this Thursday, December 11th, at 10 am PST / 1 pm EST / 5 pm UTC for Apache Ignite 3 and GridGain 9 for Spring Boot and Spring Data Development Training. During the training, you will build a RESTful web service that uses Apache Ignite as an in-memory database. The service is a Spring Boot application that interacts with the Ignite cluster via Spring Data repository abstractions. The training will cover: - Configuring an Apache Ignite/GridGain cluster that uses Spring Boot and Spring Data - Designing Java POJOs for Ignite/GridGain Spring Data repositories - Defining custom SQL queries for Ignite/GridGain Spring Data repositories - Designing DTOs (data transfer objects) for Ignite/GridGain Spring Data services - Building a Spring Boot RESTful endpoint that works with Ignite/GridGain Spring Data services - Learning tips and tricks while working with Ignite/GridGain Spring Data and Ignite/GridGain Spring Boot Register now! https://lnkd.in/eZ45Pbuc #InMemoryComputing #ApacheIgnite #IgniteTraining

💡 How do you evolve your data schema when downtime isn’t an option? In our latest blog post, we break down how Apache Ignite 3 handles schema evolution under real-world operational pressure, keeping clusters consistent, responsive, and fully online even as data models change. If you're building high-performance, always-on applications, this deep dive shows how Ignite 3’s architecture is designed to make schema updates seamless and predictable across your entire cluster. 👉 Read the full post to explore the architecture and design decisions behind zero-downtime schema evolution: https://lnkd.in/eUH2WaRP #ApacheIgnite #DistributedSystems #RealTimeData

Your database schema needs to evolve, and your 24/7 production system can't stop. - New compliance field? Two-hour maintenance window.  - International expansion? Coordinate five teams, pray nothing breaks, lose six figures in revenue while production sits idle. Every distributed system I've worked with treats this as inevitable. Schema changes mean downtime. Downtime means lost sleep and revenue impact. That's just how it works. Except it's not. Apache Ignite separates schema metadata from data storage. Schema changes complete in milliseconds as atomic metadata operations. Applications see new columns immediately. Existing queries keep running. No coordination across replicas, caches, and pipelines. No downtime. The trick is how Ignite 3 handles in-flight transactions during schema evolution. I wrote about the architecture in detail. Read the full post: Schema Evolution Under Operational Pressure (https://lnkd.in/gN3Ev-F2) #ApacheIgnite #DistributedSystems

Join GridGain Sales Engineer, Nelzir Louiseize, on Thursday, December 11th, at 10 am PST / 1 pm EST / 5 pm UTC for Apache Ignite 3 and GridGain 9 for Spring Boot and Spring Data Development Training. During the training, you will build a RESTful web service that uses Apache Ignite as an in-memory database. The service is a Spring Boot application that interacts with the Ignite cluster via Spring Data repository abstractions. The training will cover: - Configuring an Apache Ignite/GridGain cluster that uses Spring Boot and Spring Data - Designing Java POJOs for Ignite/GridGain Spring Data repositories - Defining custom SQL queries for Ignite/GridGain Spring Data repositories - Designing DTOs (data transfer objects) for Ignite/GridGain Spring Data services - Building a Spring Boot RESTful endpoint that works with Ignite/GridGain Spring Data services - Learning tips and tricks while working with Ignite/GridGain Spring Data and Ignite/GridGain Spring Boot Register today! https://lnkd.in/eZ45Pbuc #InMemoryComputing #ApacheIgnite #IgniteTraining

High-velocity data workloads demand more than traditional disk-first databases can deliver. 🚀 Check out our new blog, which breaks down how Apache Ignite 3's memory-first architecture unlocks microsecond performance while maintaining durability, a game-changer for real-time analytics, IoT, and event-driven systems. 👉 Explore how memory-first design removes the speed-vs-reliability trade-off: https://lnkd.in/eahtQaqU #RealTimeData #ApacheIgnite #BigData

If you're processing 10,000 events per second and each transaction takes 15ms of disk I/O, you need 150 seconds of processing time every second. That's not a scaling problem. That's arithmetic. Traditional disk-bound databases force this constraint. Every transaction hits the WAL. Every write waits for fsync. Your throughput ceiling no longer has much to do with CPU or memory. Disk I/O is the constraint. Apache Ignite sidesteps this entirely with a memory-first architecture. Event data lives in memory for immediate access. Persistence happens asynchronously in the background. Those 7-25ms disk operations drop into the sub-millisecond range while retaining ACID guarantees. I dive deeper into the benefits of a memory-first distributed database in my post. https://lnkd.in/gC49QHRj #ApacheIgnite #DistributedSystems #DistributedComputing #InMemoryComputing

💡 The next generation of distributed data is here — and it’s built for the speed, scale, and reliability today’s modern applications demand. Apache Ignite 3 introduces a redesigned architecture focused on simplicity, consistency, and high-performance data processing across distributed systems. If you’ve been following the evolution of Ignite, this deep dive into its new architecture is a must-read. From a unified schema-centric design to a more modular, predictable, and developer-friendly foundation, Ignite 3 sets the stage for more efficient transactional and analytical workloads. 👉 Explore Part 1 of the architecture series to see what’s new, why it matters, and how it improves the developer and operator experience. 🔗 https://lnkd.in/enRQeMyt #ApacheIgnite #DistributedSystems #DataEngineering

Your high-velocity application started with smart choices: A database like PostgreSQL for transactions, Redis for caching, custom processing for domain logic. Those decisions powered your early success. Then success changed the math. At 100 events per second, 2ms of network latency between systems is noise. At 10,000 events per second, that same 2ms creates a 20-second processing backlog. During traffic spikes, traditional architectures either drop connections or miss SLAs. Neither is acceptable. The problem isn't any single system. It's the *compound effect* of moving data between them. Apache Ignite takes a different approach: process events where the data lives. Memory-first storage with ACID guarantees. Collocated compute that eliminates inter-system network latency. One schema driving transactions, analytics, and compute operations. The architecture that fueled your growth doesn't have to become your scaling limit. Read my blog post: When Multi-System Complexity Compounds at Scale https://lnkd.in/gqjuGSvs) #ApacheIgnite #DistributedSystems #DistributedComputing #InMemoryComputing