It’s no question that Cloud is no longer a passing phase. In the span of a few years, Cloud has moved from an interesting concept to a useful business tool. What began as a creative tool for testing has moved into the mainstream as a way to improve hardware utilization and expand capacity. The benefits for Cloud are well established, and more customers are moving to consumption-based models, either with captive or public Cloud solutions. Many tools exist to help with Cloud migrations, but few have the flexibility to “see through the Cloud” to the application code, and make that code fit this new world.
At CAST user group meetings, which we conduct annually in key regions, I’m always amazed by what our customers are doing with software analytics. Something so foundational – the measurement of software performance – yields such powerful results for Fortune 200 companies that are on a constant hunt to meet business demands and beat out the competition. This year’s user groups are special, because CAST is celebrating our 25th anniversary. That’s how long we’ve been helping make software a little less invisible to developers, architects and business executives whose livelihood depends on software quality.
Fintech is the hot new thing. It’s the industry that will carry the UK through Brexit. It’s the latest wave of startup mania in NYC. It’s becoming the darling of Silicon Valley. Chinese tech investors are all over it. It’s fresh. It’s sexy. But, wait a minute. What is Fintech?
Recently I attended MIT’s Fintech conference (#MITFinTech). We heard Brad Peterson, CIO of NASDAQ, talk about his firm as the original Fintech founded 45 years ago. Brad told us that NASDAQ no longer thinks of itself as an exchange, but as a Fintech company. A couple MIT professors told us there are 1800 Fintech companies out there today, and that number is quickly growing. There are some that promote robo-advisors as autonomous correctors for investor freak-out during volatile markets, and others that collect live market data from the web in order to predict real economic indicators, as opposed to statistics collected by government technocrats. Blockchain, we were told, is like the Internet was back in 1993.
This blog is from CAST’s keynote speech at MeGSuS’16, 3rd International Workshop on Measurement and Metrics for Green and Sustainable Software. Download the presentation here.
Fueled by our growing thirst for constant connectivity and the dawn of the Internet of Things, the energy required to power all the world’s computers, data storage and communications networks is expected to double by 2020 according to the latest research by McKinsey & Company. This would increase the total impact of IT technology, in terms of global carbon emissions, by at least 3%.
There is more data to manage today than ever before, and this is creating an increasingly pounding headache for business executives that no dose of aspirin will soon relieve. With so many different forms of data and ways of storing that information within the organization, new data management methodologies are needed to make sense of this mind-numbing flood also known as Big Data.
Enter NoSQL. Differing from its much older and experienced brother – SQL – NoSQL has come onto the scene as the “new” and “hip” database paradigm (much like we talk about the Millennial generation). Also known as “Not Only SQL”, NoSQL is a flexible approach to data management and design that is useful for very large sets of distributed, unstructured data.
Software has always been risky business compared to more mature industries such as telecommunications and manufacturing. Historically, software has seen more canceled projects, higher costs and more frequent schedule overruns than any other industry.
Today in 2016 we are also on the forefront of receiving an increasing amount of cyber-attacks in many different forms such as denial of service, data theft, phishing and the like. Of course, other industries are also risk prone, such as banking and finance as seen by their many failures circa 2008. Indeed the insurance industry centers around risk and has developed sophisticated actuarial methods for predicting the costs of risks and when they will occur.
While you’re reading this article, if you come across words – and even sentences – that you don’t understand, there’s a high chance you feel like developers do when they’re looking at lines of code with a high level of nested complexity. A high level of software complexity can make it difficult to determine architectural hotspots where risk and cost emanate.