An Overview of In-Memory Computing for High Performance Financial Applications
Get a practical overview of how in-memory computing helps financial teams deliver real-time analytics, ultra-low latency transactions, and scalable performance for modern workloads.
- Understand in-memory computing, RAM speed, and distributed parallelization.
- See how firms accelerate STP, reconciliation, and trade processing.
- Explore high-frequency trading and real-time backtesting use cases.
- Learn how banks run real-time risk analytics at massive scale.
- Improve fraud detection and mobile payment experiences in real time.
- Compare caching, data grids, in-memory databases, and streaming approaches.
About this eBook
If you support high-performance financial applications, you are constantly balancing latency, throughput, scale, and regulatory pressure. This ebook explains how in-memory computing helps teams meet those demands by moving data closer to compute (RAM) and scaling via clustered parallel processing.
Inside, you’ll get plain-English explanations plus financial-specific examples, including straight-through processing (STP), high-frequency trading, real-time backtesting, real-time risk analytics, mobile payments, and fraud protection. It also breaks down common categories like in-memory database caching, in-memory data grids, in-memory databases, and in-memory streaming, so you can map the right approach to your architecture.
The ebook also explains how an in-memory computing platform, like GridGain In-Memory Data Fabric built on Apache Ignite, can fit into an existing IT ecosystem without forcing a rip-and-replace, while supporting transactional workloads, streaming, and fast analytics.
In-memory computing is about two things: making computing faster and scaling it.
An Overview of In-Memory Computing for High Performance Financial Applications
See how in-memory computing enables speed and scale for financial services, from straight-through processing and backtesting to real-time risk analytics and fraud detection.
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