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Java · Performance · In-Memory Computing

In-Memory Computing with GridGain
for Real-Time Analytics

How we removed data-volume bottlenecks from a real-time analytics platform with a GridGain in-memory computing layer - delivering 5x faster processing.

ClientConfidential (NDA)
IndustryData & Analytics
Core stackJava, GridGain, Apache Ignite
EngagementPerformance engineering
The challenge

The volume and velocity of data were overly challenging for the existing real-time analytics platform, resulting in serious bottlenecks that caused lateness in insights and hence loss of opportunities.

Our solution
→ In-Memory Data Grid: Stored and processed data in real time, reducing latency and improving performance across the platform.
→ Distributed Computing: Scaled processing of huge data volumes across clustered nodes instead of a single bottlenecked path.
→ ACID Compliance: Guaranteed data consistency and reliability throughout the distributed architecture.
Results achieved
5×
Faster processing speed for analysis and data throughput
3×
Reduction in latency, enabling timelier decision-making
2×
Increase in data processing via the scalable architecture

*Due to the terms of our Non-Disclosure Agreement (NDA), the client's identity and website URL remain confidential.

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Technologies used
GridGain 8.8 Apache Ignite Java 11
Key metrics
Processing speed5×
Latency reduction3×
Data processing increase2×
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