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Increased competitive pressure and large volumes of fast-changing data have created huge challenges for today's enterprises. Thanks to a convergence of computing advances and a new generation of software, businesses can now keep up with scalability demands and leverage their server farms. As a result, companies can turn today's data challenges into opportunities to out-perform competitors.

Over the past several years, distributed in-memory computing has emerged as a powerful means to gain competitive advantage for nearly any business. Increases in data volume, complexity and rate-of-change have driven wide adoption of scalable computing solutions like server farms and compute grids so applications can handle increased computing loads. Keeping up with these trends is daunting in itself. Focusing on the need for faster applications often means important opportunities are overlooked. Creative analysis of changing data to recognize and respond to trends is now a must-have capability. Previously, only large companies with deep pockets could afford the technology and training necessary to seize such opportunities.

Never before has computational speed played such a pivotal role in gaining competitive advantage. For example, the financial services market provides the most dramatic example of this new reality. Market data messages have skyrocketed over the past few years as automated trading has become widespread. Marketdatapeaks.com tracks daily market data messages flowing across live financial data feeds and current volumes range between 2 and 3 million messages per second. Automated systems are faced with analyzing this staggering amount of data in as close to real-time as possible to make profitable trading decisions.

Opportunities are measured in microseconds, making computing technology a key competitive factor. Even for business segments that do not face such extreme parameters, it is clear that scalability and computational speed now create important advantages. For example, online retailing, auction sites, entertainment, gaming, and reservations systems all demand scalability and can benefit from insights into their data sets.

Data analysis is a powerful tool for business expansion and increased customer satisfaction. Gleaning useful information from line of business data like buying trends, warranty claims, customer feedback, manufacturing efficiency, and a host of other data can provide a substantial edge over others who are slow to understand their business information. However, data volumes are exploding and timeframes are shrinking, making data mining harder than ever.

A number of new technologies have been developed to help meet these challenges. Multi-core processors have overcome the limits of single processing units. Memory manufacturing advances have brought RAM prices to historic lows. Advances in networking now mean that 10-20 gigabit/second networks are common, and much faster networking is quickly emerging. Hardware virtualization is widely used in data centers as a means of increasing utilization of server hardware as well as providing fault tolerance, continuous uptime, and more.

These core technology advances are being leveraged by a new generation of distributed in-memory computing software. Combining the speed of storing data in-memory and the scalability of distributed systems, these solutions are grouped under the general heading of distributed data grids. Solutions in this category provide in-memory storage which spans a grid or server farm to scale an application's performance.

To stay ahead of the competition, companies must now take distributed data grids a step further and add data analysis capability. How can this be done? Companies can consider built-in parallel data analysis to transform the data into a parallel computation engine. Functions to analyze data in the distributed data grid can be easily written by both developers and analysts. These functions can then be invoked in parallel across the data grid to provide lightning-fast results. Importantly, parallel data analysis automatically takes full advantage of speedup across multi-processors, multi-cores, and virtual hardware instances. Best of all, the developer need not know any parallel programming. The functions are written just as if they were meant to run in-memory on a single machine and the distributed data grid does the rest. This removes one of the longstanding barriers to parallel data analysis and opens it up to nearly any business.

Using a distributed data grid, businesses can quickly and easily analyze their data sets. For example, a retailer can analyze sales trends for an online promotion while it is running and immediately make improvements. A financial services firm can store investment portfolios in the data grid and apply algorithms to them based on streaming market data, generating trades to optimize the portfolios. A manufacturing firm might cache a manufacturing line's process metrics in the data grid and run performance algorithms against the data to monitor efficiency and quickly spot problems. An online gaming site could shorten its response times to provide a more realistic gaming experience.

Finally, cloud computing is getting a lot of attention these days and it bears mentioning that there is strong synergy between in-memory solutions and cloud computing. With elasticity as a property of both the cloud and distributed data grids, it is easy to imagine provisioning a large number of cloud servers into a distributed data grid, pulling a big data set into memory, analyzing it, and then just as quickly releasing the servers. Could data analysis be a killer app for the cloud?

The benefits of distributed in-memory computing are being reaped today by many forward-looking companies. As this important technology spreads into the mainstream it is sure to become a requirement to remain competitive.

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