Skip to main content

Ignite Summit 2025 — Watch on demand

Download Ignite

Quick Start Guide

ARCHITECTURAL BACKBONE

Memory-First + MVCC + Distributed Replication

CORE FEATURES

DATA PLACEMENT & PROCESSING

OPERATIONAL EXCELLENCE

PRIMARY USE CASES

ADDITIONAL USE CASES

SUPPORTING PATTERNS

Download Ignite

Quick Start Guide

ARCHITECTURAL BACKBONE

Memory-First + MVCC + Distributed Replication

CORE FEATURES

DATA PLACEMENT & PROCESSING

OPERATIONAL EXCELLENCE

PRIMARY USE CASES

ADDITIONAL USE CASES

SUPPORTING PATTERNS

Apache Ignite

Use Cases

Fast OR Consistent? Choose Both. Apache Ignite eliminates the distributed systems trade-off through memory-first architecture, partition-aware routing, and consensus replication.

Apache Ignite Use Cases

Primary Use Cases

Eliminating The Distributed Systems Trade-off

These use cases demonstrate Apache Ignite's core differentiation: delivering both speed and consistency where traditional systems force an impossible choice.

Event Stream Processing And Enrichment

Fast OR Consistent? Enrich high-throughput event streams with consistent reference data. Eliminate cache invalidation complexity.

Microservices State Management

Simple OR Scalable? Distributed ACID transactions across service boundaries eliminate saga complexity. Infrastructure cost reduction through system consolidation.

AI/ML Feature Stores

Real-time OR Accurate? Zero training/serving skew with low-latency feature retrieval. MVCC snapshots provide point-in-time consistency.

Session Management And Caching At Scale

Fast OR Durable? Any-node session access with automatic failover and zero data loss. Eliminate sticky sessions while maintaining consistency.

Additional Use Cases

Operational + Analytical In One Platform

Apache Ignite supports operational analytics and time-series workloads while maintaining ACID guarantees and SQL query capabilities across your entire data platform.

Operational Analytics

Transactional OR Analytical? Run concurrent analytical queries on live transactional data without blocking writes. Competes with relational database MVCC and dedicated OLAP systems.

Fast Data Marts

Warehouse Latency OR Data Mart Limits? Sub-second queries on curated datasets without cache complexity. Purpose-built analytical repository with ETL-populated data and full SQL support.

IoT and Time-Series Data

Scale OR Validate? High-volume writes with schema validation and SQL aggregations. Competes with specialized time-series databases.

Supporting Patterns

Architectural Patterns For Both Versions

These patterns apply to both Ignite 2 and Ignite 3, providing architectural guidance for specific deployment scenarios.

Digital Integration Hub

An advanced platform architecture that aggregates multiple back-end systems and databases into a low-latency and shared data store.

High-Performance Computing

Schema-driven colocation and compute-to-data patterns enable local joins and recommendation engines with significant latency reduction through colocation.

Key-Value Store

Access the cluster with key-value requests using Ignite 3's Table API with RecordView and KeyValueView, or explore Ignite 2's Cache API for legacy deployments.

Database with Memory-First Storage

Database-first platform with multi-tier storage (aimem, aipersist, rocksdb) and full SQL support. Concurrent transactional and analytical workloads with flexible storage placement.

Apache Ignite 2.x Use Cases

Legacy Use Cases
For Apache Ignite 2.x

These use cases apply to Apache Ignite 2.x, which remains actively supported. Each page includes patterns and APIs specific to Ignite 2's architecture.

In-Memory Cache

Distributed in-memory cache accelerates applications and databases. Cache data with SQL queries and ACID transactions using cache-aside or read-through/write-through strategies.

In-Memory Data Grid

Advanced read-through/write-through cache deployed on top of multiple databases. Colocation enables low-latency computing with data stored in-memory.

Apache Spark Acceleration

Accelerate Spark applications by keeping data in a shared in-memory cluster. Minimize data shuffling with Ignite's RDD and DataFrame implementations.

Apache Hadoop Acceleration

Real-time analytics across Hadoop operational and historical data. Use Ignite as a high-performance data access layer for low-latency operations.