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A Short History of Programming - Part 3 | @DevOpsSummit #AI #ML #DevOps #FinTech #Blockchain
Programming for commerce, banking, and FinTech
By: AppDynamics Blog
Feb. 11, 2017 04:00 PM
Code Compiled: A Short History of Programming - Part 3
Look at how far we’ve come. Just seven decades ago, the word “computer” referred to someone in an office with a pile of charts and a slide rule. Then ENIAC, the first electronic computer, appeared. It was followed quickly by the Commodore 64 personal computer and later, the iPhone. Today, the Sunway TaihuLight crunches data by combining 10,649,600 cores and performing 93 quadrillion floating point operations every second. Where will we be in another seven decades?
In part one of this series, we covered how the evolution of hardware shaped the development of programming languages. We then followed up in part two with the impact of the software created by those languages. In this final blog in the series, we’ll look at how programming has taken the leap beyond computers into devices, with an emphasis on how programming is rewriting the rules of commerce, banking, and finance.
Technology in Society Over the Past Decade
The single biggest driver of internet access growth has been mobile devices. Simple cell phone ownership went from around 70 percent by American adults in 2006 to 92 percent a decade later. Smartphones, as well as the vast data-crunching resources made available by the app ecosystem, went from an ownership rate of 35 percent just five years ago to 68 percent today. Tablets have taken a similar explosive trajectory, going from 3 percent ownership in 2010 to 45 percent today.
This growing hunger for mobile devices required exponentially more data processing power and a vast leap in traffic across wireless networks. The growth rate can only be described in terms of zettabytes (trillions of gigabytes). In 2009, the world had three quarters of one zettabyte under management. One year later, data generated across networks had nearly doubled to 1.2 ZB, most of it enterprise traffic. By the end of last year there were 7.9 ZB of data generated and 6.3 ZB were under management by enterprises. In the next four years, you can expect there to be 35 ZB of data created by devices, with 28 ZB managed by enterprises.
Developers have had to work furiously to restructure their approach to software, databases, and network management tools just to avoid being swamped in all this data.
A Brief History of Financial Record Keeping
In the 1980s, the financial industry was still dominated by mainframes. The personal computer revolution that was sweeping the rest of the business world didn’t impact finance. Huge servers and clients were the only way to manage the vast amount of data (compared to processor speeds at the time) that had to be crunched. You might have to use COBOL or write SQL queries against a DB2 database to get the financial answers you needed to make the right business decisions (versus what is possible today). Mainframes were normally closed systems with applications written specifically for them by outside consultants.
All that changed dramatically in the 1990s, with the growth of faster servers, open systems, and the connectivity of internet protocols. Mid-sized computers for business gained immense processing power at lower costs. Mainframes began to be repurposed for back-end processing of transaction data as the finance industry consolidated batch-processing projects like billing.
Computers like the IBM AS/400, which had run on IBM proprietary software in the past, gained the facility for running financial software like SAP, PeopleSoft, and JD Edwards. By the late 1990s, the appearance of Linux and virtual machines running inside mainframes opened up the entire finance sector to a flurry of new open-source development projects.
Simultaneously, network connectivity to the internet and then the web opened up financial data providers to a new threat from outside: hackers. Before, password management and inside jobs were the biggest threat to financial data security. Connectivity opened a window to a new generation of cybercriminals.
Programming Challenges for Data Security
The biggest problem is that attacks like these are increasing in their intensity and sophistication. In fact, 2016 saw a 458 percent jump in attacks that searched IoT connections for vulnerabilities. Meanwhile, another front has opened up on employee mobile devices for enterprises. Last year alone, there were over 8 billion malware attacks, twice the number of the year before, most of which went after weaknesses in the Android ecosystem. In terms of the data most sought after by hackers, healthcare businesses registered slightly more attacks than even those in the financial industry.
Data-security experts have to stay ahead of risks from both the outside and the inside, whether they are malicious or accidental. Both can be equally devastating, regardless of intent.
Ian McGuinness recommends six steps for security experts to help them concentrate on covering as many vulnerabilities as possible early on, before moving on to custom development:
These are really just the basics, though. Monitoring network traffic, recognizing malicious code, and responding in time to make a difference represent the biggest challenges for the future.
What’s Next for FinTech
On the road ahead, among the top challenges for FinTech developers will be:
The FinTech panel at one recent AppDynamics event concluded that:
“All banks have the same problems, but the capabilities to solve these problems have changed. Banks are taking different approaches, but the endgame is the same, making sure the customers can access their money when they want and how they want.”
Imagining the Future
The post Code Compiled: A Short History of Programming — Part III appeared first on Application Performance Monitoring Blog | AppDynamics.
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