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Event Stream Processing
And Enrichment

Fast OR Consistent? Choose Both.
Enrich high-throughput event streams with consistent reference data
Apache Ignite Use Cases

The Enrichment Bottleneck

Your streaming pipeline processes thousands of events per second. Each event needs enrichment with customer data, product catalogs, or pricing rules. The enrichment lookup becomes the bottleneck. Cache the reference data and risk stale enrichments. Query the database and watch latency spike under load.

Cache invalidation adds operational complexity without solving the freshness problem. Database round-trips accumulate into seconds of delay across your event stream. Neither approach delivers both the speed and accuracy that production workloads require.

How Apache Ignite Solves This

Apache Ignite eliminates the latency-consistency trade-off through memory-first architecture with ACID guarantees

Low-Latency Lookups

Memory-first architecture with partition-aware routing delivers microsecond-to-millisecond lookups. RecordView API provides direct partition access without coordinator overhead, delivering significant latency reduction for reference data enrichment.

Strong Consistency

ACID guarantees with consensus replication eliminate cache invalidation complexity. Stream processors always read consistent reference data. No eventual consistency windows. Colocation support enables local joins for further performance optimization.

Architecture Pattern

Event Enrichment Without Cache Invalidation

Stream processors read reference data directly from Apache Ignite using partition-aware routing for low-latency lookups with ACID consistency.

Integration Pattern: Streaming platforms process events, enriching each event by looking up reference data in Apache Ignite through RecordView API.

Consistency Model: Consensus replication ensures writes to reference data propagate to all replicas with strong consistency. No eventual consistency delays.

Performance Characteristics: Memory-first architecture delivers microsecond-to-millisecond lookup latency at high throughput. Partition-aware routing eliminates coordinator overhead.

When This Pattern Works

This architecture pattern is best for:

  • High-throughput event stream enrichment
  • Reference data that changes infrequently or requires strong consistency
  • Real-time processing where cache staleness creates business risk
  • Systems where cache invalidation complexity becomes operational burden

Example Use Cases:

  • Financial Trading: Enrich order events with current instrument data, margin requirements, and risk parameters
  • E-commerce: Enrich clickstream events with product catalog, pricing, and inventory status
  • Fraud Detection: Enrich transaction events with customer profiles, risk scores, and historical patterns

Key Benefits

Eliminate Cache Invalidation

ACID guarantees replace cache invalidation complexity. Stream processors read consistent reference data without cache warming, TTL tuning, or invalidation logic. Updates propagate through consensus replication, not cache invalidation messages.

Low-Latency At Scale

Memory-first storage delivers microsecond-to-millisecond latency for reference data lookups. Partition-aware routing bypasses coordinator overhead. Horizontal scalability handles throughput growth without latency degradation.

Strong Consistency

Consensus replication ensures reference data updates propagate with strong consistency. No eventual consistency windows. Stream processors never enrich events with stale reference data.

System Consolidation

Single platform replaces separate caching and database systems for reference data. Reduces infrastructure complexity and operational overhead. Eliminates synchronization between cache and database layers.

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