Big Data Driving Middleware Adoption

Industry research firm IDC defines the digital universe as a measure of all the digital data created, replicated and consumed in a single year and estimates that it is on track to grow 50-fold between 2010 and 2020. Organizations today are not only concerned about how to store tremendous amounts of data, they're also struggling to analyze it. They need to harness the increased volume, variety and speed of data if they are going to succeed.

Businesses run on information, but big data introduces data sets so large and complex that storing them for easy retrieval is cumbersome. For the foreseeable future, organizations will continue to rely on infrastructure specifically designed for big data applications to run reliably and scale seamlessly to keep up with the pace at which data is generated or transferred.

Platform Modernisation

According to a recent survey by The Linux Foundation, organizations are continuing to expand their reliance on Linux to support mission-critical applications and the rise of big data. Enterprise platforms like Red Hat Enterprise Linux integrate high performance, scalable storage and data throughput with the ability to successfully develop, integrate and secure applications consuming data.

Indonesian Stock Exchange (IDX) halved its annual hardware investment costs after switching from UNIX to Red Hat Enterprise Linux. With the rapid growth of the Indonesian capital market and the demanding development of its online trading system, IDX needed an IT system that could deliver superior performance, capacity, and security, but was concerned that the high cost of IT investments could result in a high total cost of ownership (TCO). To solve this problem, IDX chose Red Hat to provide a less-costly, reliable, stable, secure, and scalable trading system.

Big Data Driving Middleware Adoption

IT departments struggling to design and implement solutions capable of managing exponential data growth while meeting strict requirements for application scale and performance are turning to in-memory data grids (IMDGs). According to Gartner, 40% of large enterprises will deploy one or more IMDGs by 2014.

As organizations increase the volume, velocity, and variability of the type of data they collect, process, and manage, traditional database designs are hard pressed to keep up. This presents challenges related to scalability, infrastructure design, security, timely access for critical decisions, and increased costs due to the increased complexity.

 

In-memory data grids meet those additional considerations and provide a reliable, transactionally-consistent, distributed in-memory data store. They can be used to incrementally extend the performance and scalability of established applications. They can also be used to implement brand new high-performance/high-scale applications - all based
on the idea of using main memory for fast access, distributing data to scale and working with another master data repository or maintaining duplicate, remote nodes to provide resilience and persistence.

Ultimately, these benefits empower the business to better meet customer service expectations, maintain application performance through peak demand cycles, respond to business opportunities in real time, and make better use of developer resources by freeing up application developers to concentrate on making applications versus vying for database resources.

As a leader in the run-time application servers that power numerous high-scale applications, Red Hat has been close to the challenges posed by real-time access to big data, specifically its effect on application performance. Red Hat has developed Red Hat JBoss Data Grid to address these challenges.

Red Hat JBoss Data Grid is designed to provide a way to scale the data tier without an expensive rewrite, using the expertise you have in-house. This can give enterprises a flexible and cost-effective way to improve application performance and get more out of their mission-critical applications.

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