Shris has extensive financial domain experience and expertise in advanced technology solutions in order to provide software and infrastructure services for the financial industry. Our team has extensive knowledge and experience in providing solutions to major Wall Street financial companies in the areas of Trade processing, Risk management, Compliance, Surveillance, Regulatory reporting, Financial Analytics, and Monitoring.

Shris has built a proprietary solution for high throughput complex event processing engine using cutting edge open source frameworks. The framework could be used to build customized solutions to its clients to solve specific business problems. Additionally, Shris has extensive knowledge and experience in deploying solutions on cloud in order to be able to provide linearly scalable infrastructure.

The GigaProc framework is a great solution to meet some of the following challenges faced by the financial industry.

Slower overall application performance: A real-time transaction processing solution not designed to handle high volume and high throughput processing.

Lack of support for BigData Analytics: Difficulty in retrieving and finding meaningful information from a plethora of data Inability to get timely access to information.

Incoherent Visualization: Inability to visualize data and unresponsive applications to visualize BigData. Stale Data: The risk of stale data is very high without a highly scalable, highly available processing engine

High overall costs: Overall operating costs for computer hardware and software skyrocket with BigData. A high performing engine such as HTP engine means lesser number of servers to achieve higher throughputs.

Some of the major Benefits of GigaProc are:

Handle hundreds of thousands of events per second: GigaProc engine is designed to processes and analyzes huge volume of events, with the ability to scale up on demand. The rate of events at any given time can be highly unpredictable and our engine has capabilities to gracefully deal with spikes in message load.

Aggregate and correlate data in multiple streams: GigaProc engine also has the ability to relate and combine data from multiple sources as they arrive, in real time. This is possible without introducing any additional latency due to the extra processing involved.

Aggregate streaming data with historical or related data: It is not enough to get a view of just fast moving streaming data. Sometimes, to make sense and enable decision making, applications need views that combine current data with historical streaming data or data originating from other enterprise data repositories. GigaProc engine together with Complex Event Processing (CEP) achieves this objective as well.

Distributing the derived events to client applications: GigaProc engine not only does continuous analysis of streaming data but also is capable of distributing (pushing) the derived events to remote enterprise applications that may be distributed on over intranet/internet in some cases. Providing resilient client connectivity and reconnection mechanisms are critical functions that GigaProc engine performs.

Guaranteed Quality of Service (QoS): For real-time applications that sense and respond to simple events, it is critical that the derived events be delivered within a configured time interval. For instance, in very low latency applications, events should be analyzed and results delivered to clients within a few microseconds to milliseconds. GigaProc ensures that.

High availability: High availability of services and applications with huge data volumes is a concern that needs to be addressed by enterprises. The GigaProc engine provides high availability for the data it manages through flexible replication strategies. It also ensures that all run time components are resilient and provide automatic failover services. All incoming transactions are inserted into a Big Data database (Cassandra now, can be customized according to client requirements). Both fine-grained, as well as coarse-grained Big Data, can be used to generate historical reports, business analytics. Historical search can also be performed on the archived Big Data.

Scalability: Extreme scalability is needed to process Big Data events and transactions with high throughputs and low latencies. Full use of multiple core, multiple server configurations is made by GigaProc engine to ensure the data is processed correctly in a cluster of computers.

Display the Real-time data in Dashboards: With our advanced GUI solution including customizable dashboards, we are able to display both real time as well as historic information. Real-time information is displayed as and when the data is received.

Framework Highlights:

Shris has developed a distributed computational framework called GigaProc, which can be used for high throughput and low latency data processing. GigaProc provides a means to capture high volume of data, process the data across n number of distributed nodes parallely, persist the data for analysis and reporting. Coupled with the ability to deploy and run solutions based on this framework on Cloud infrastructure, GigaProc provides for a cost effective and viable framework to meet many of the client application demands.

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