Routinely, organizations collect terabytes of security-relevant data and information, such as software app events, network events and action events of people for regulatory compliance as well as post hoc forensic analysis. Big companies gather an estimated ten to one hundred billion events daily. The numbers only grow as companies allow event logging in more sources, deploy more devices, run more software and hire more employees. Sadly, the variety and volume of data could rapidly become overwhelming.
THE BIG DATA ANALYTICS TREND
Data analytics, the big-scale analysis and information processing, is in active use in various fields and recently, has drawn the interest of the security community for its promise to analyze as well as correlate data that’s security-related in an efficient and unprecedented scale.
A SECURE DEVELOPMENT LIFECYCLE
Security practices integration in the software development lifecycle and verifying security of internally developed apps before they’re deployed could help in mitigating the risk from both internal and external sources. The SDL or security development lifecycle is a software development security assurance procedure that consists of security practices clustered by six phases, including training, design and requirements, testing, the construction, the release and then the response. One of the relevant steps in securing development is to integrate testing tools as well as services in the SDL. The tools enable developers to model an app, scan code, check quality and make sure that it meets the regulations. Automated secure development testing tools could help designers and developers determine and fix security problems. A development process that is secure could be integrated to both traditional SDL and the fast pace agile development.
DESIGNING SOFTWARE SCALABILITY WITH BIG DATA
Software security issues are not new. With so many unscrupulous people invading the web, data has never become more vulnerable. That is why it is very important for business organizations and even individuals to secure sensitive information. To mitigate the associated risks with technology and software development, an iterative, systematic approach is required to ensure that database selections and initial design models could support long-term scalability as well as analysis requirements of a big data app. A modest investment in the upfront design could create unprecedented ROI when it comes to greatly minimized redesign, operational expenses and implementation in the long run of a big-scale system.
Since the scale of the target system prevents creating full-fidelity prototypes, a software engineering approach that’s well-structured is needed in order to frame technical concerns, quickly build and execute important but focused prototypes and determine decision criteria architecture. Without a structured approach, it’s easy to fall in the trap of going after a deep comprehension of the underlying technology rather than answering a major go/no-go queries on a certain candidate technology. The aim of this should be acquiring the right decisions for a minimum cost. Secure development models should be top priority when it comes to developing software solutions.
VISIBLE USES FOR DATA ANALYTICS
One of the most visible uses for data analytics is fraud detection. Phone companies and credit card companies have conducted big-scale fraud detection for years. Nonetheless, the custom-built infrastructure important to mining large data for detecting fraud was not economical enough for wide-scale adoption. Today, huge data tools are improving in available information to security analysts via consolidating, correlating and contextualizing much more diverse sources of data for longer times. There are specific benefits from the system’s tools.
THE CONNECTION OF BIG DATA AND SOFTWARE DATA
Processing sets of data of over five petabytes, particularly those integrating unstructured data from various sources, needs really fast processors, coupled with sophisticated analytics software for identifying the associations and patterns that give meaningful feedback. Furthermore, enterprises should have a mechanism for visualizing the information.
Over the past ten years, a cadre of innovative organizations developed technologies for accomplishing goals and wrapped them to analytics platforms, which already are revolutionizing decision making in other areas, which are based on facts.
Organizations that are focused on quality acknowledged the challenges of visualizing and harnessing huge data were a close parallel to the difficulties that software development teams have experienced. While a software project activity data may seem a world away from big data, it shares little noteworthy similarities with unstructured data. It’s not easily accessible natively, nor organized properly for reporting and data analysis and often is also unstructured.
Big data is a trend these days that are used by many organizations anywhere they may be in the world. Developing software solutions has become more important these days with the huge amounts of data and information gathered and accumulated. Data analytics have made efforts in data storage with new technologies.