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Finnish Online Brokerage Invests in Application Monitoring

A complex environment and rapid growth necessitated the replacement of a homegrown application monitoring tool.

Online Finnish brokerage and financial services company eQ has deployed Hyperic HQ Enterprise to monitor and manage its large, heterogeneous web environment to keep service levels high for more than 55,000 customers.

Founded in 1998 as online brokerage firm, eQ has since evolved to offer asset management and banking services as well. With a complex IT environment consisting of 250 physical and virtual machines, a wide variety of custom and proprietary banking software and a mix of legacy and newer web technologies, eQ needed an efficient way to guarantee application and infrastructure performance on a 24/7 basis.

As its IT infrastructure grew and diversified and its homegrown monitoring solution became too complex to customize, eQ needed to find a new monitoring and management tool that could be up and running quickly and could handle the unique monitoring needs of a banking operation. After determining that the "Big 4" proprietary solutions were complicated, expensive and required too many resources to run and that most open source alternatives lacked the needed enterprise-class functionality, security and support, eQ turned to Hyperic HQ Enterprise.

eQ's environment is a broad mix of technologies, including Windows, Linux and Solaris operating systems, Apache and Sun web servers, VMware virtualization and MySQL, Oracle, and PostgreSQL databases. Hyperic supports all of these technologies out-of-the-box. eQ's environment is also full of commercial and homegrown banking software. In addition to Hyperic's ready-to-run resource plugins, HQ's open framework makes it simple for eQ to create custom plugins as needed to incorporate their finance-specific technology into HQ's monitoring reach.

HQ metrics have been used to problem solve within eQ. When eQ faced a slowdown in data transfer within its market information solution, it was important to prevent the localized issue from snowballing into a larger IT fire. However, slogging through log data to do so would have been time-intensive and inefficient. Instead, metrics collected from HQ were used to determine the location of and reason behind the problem so that eQ could resolve it before it led to performance glitches for end-users.

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