Misys Uses GridGain to Enable High Performance, Real-Time Data Processing

Misys Case Study

Headquartered in London, Misys is transforming the global financial services industry by making financial institutions more resilient, more efficient and more competitive. The company provides the broadest, deepest portfolio of financial services software, covering retail and corporate banking, lending, treasury, capital markets, investment management and enterprise risk. Misys has more than 2,000 customers in 130 countries, addressing industry requirements at both a global and local level. Their solutions are used by 48 of the world’s 50 largest banks and 12 of the top 20 asset managers.


As a large financial technology company, Misys solutions must manage huge amounts of trade and accounting data. To meet evolving customer demands for real-time services and satisfy evolving compliance and reporting regulations in Europe, Misys opted to implement a new Java-based IT stack that will support the use of data lakes instead of traditional databases.


To handle the caching of data from the data lake and distributing the cached data across a network cluster for massive parallel processing, Misys opted to deploy GridGain.

“With GridGain, we have achieved real-time processing of massive amounts of trade and transaction data, eliminating bottlenecks and enabling us to offer next-generation financial services to our customers,” said Felix Grevy, Director of Product Management for FusionFabric.cloud at Misys.

The GridGain In-Memory Data Fabric, based on Apache® IgniteTM, enables high-performance transactions that run up to 1,000,000x faster than disk-based approaches. It provides high speed transactions, real-time streaming and fast analytics in a single, comprehensive data access and processing layer which works with any common RDBMS, NoSQL or Hadoop database. GridGain provides ACID transaction guarantees and is ANSI SQL-99 compliant. The solution powers existing and new applications in a distributed, massively parallel architecture on affordable, commodity hardware, which can be easily scaled by adding more nodes to the GridGain cluster.

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