Where Big Data and Software Development Collide

Corporate America is turning to data analytics, machine learning, and business intelligence to answer the following questions of business operations, marketing campaigns, and other important management decisions:

·         What’s wrong?

·         What’s going to happen next?

·         Is this going to work?

In order for these questions to be answered accurately and in a timely manner, programmers are being asked to develop software capable of harnessing the full potential of Big Data.

Massive data centers filled with exabytes (one billion gigabytes) of transaction records, financial information, browsing habits, social media activity, and mobile data are impotent without software developers writing programs to facilitate the analytics process. 


Developers can then, in turn, use predictive analytics to find and fix bugs faster and make product/software testing more efficient.

Aspiring developers can prepare for programming positions in data-heavy organizations or cloud service providers with a Master of Science in Software Development degree. With more companies becoming dependent on data analytics, developers with postgraduate diplomas and well-rounded knowledge of Big Data programming languages and technologies continue to be in high demand.

Collaboration And Streamlining

Software development is a collaborative activity, relying more and more heavily on communication between departments, managers, and even competing companies. Because of this, the new career path of DevOps is evolving into a formidable field. DevOps (development operations) personnel automate software integration with the cloud and various platforms, expedite program and product testing, and carry out development tasks.

“Developers with experience in agile development practices and IT operations requirements are in demand, because they can fill the growing need for DevOps professionals, who are particularly important for cloud computing initiatives,” explains CompTIA director Seth Robinson in journalist Mary K. Pratt’s “4 High-Growth Tech Fields With Top Pay” on CIO.com.

Robinson continues, “Programmers comfortable working with the full range of technologies needed to support a product – so-called full-stack developers – are increasingly in demand.”

Cloud software solutions, such as SaaS (software-as-a-service), hasten the entire software development process by incorporating the classic text editor, compiler, debugger, and other programmer tools into an integrated development environment (IDE). Additionally, programmers are now encouraged to reuse proven programs or program elements through open-source sharing on repositories. GitHub is one of the most popular of these repositories.

“Transferring major services and applications to the cloud has created new demands for productive software development,” writes scholar George Kylaktopoulos, et al., in “An Overview Of Platforms For Cloud-Based Development” on the National Center for Biotechnology Information website. “Cloud concepts and technologies provide a valuable substrate to support software development environments ‘in the cloud, for the cloud’ as they can provide an ample pool of compute resources for code development and testing, and code repositories to support developer collaboration, a key driving force to software productivity.”

Predictive Analytics And Software Testing

Programmers are invaluable to companies that deal in data. And data analytics are also being incorporated into more traditional programming tasks. Essentially, Big Data’s continued momentum is affecting every aspect of the software industry.

Developers are being called upon to program predictive analytics systems that can be seamlessly deployed into both software applications and business processes. Web services and predictive modeling relies on programmers in order to pull the correct data, categorize it appropriately, and run the algorithms that will produce the insights sought by a company’s decision-makers.

“Automated predictive analytics processes will help testers understand the impact of changes made in the development stage across the entire software development cycle, identify the amount of testing needed to produce a minimum viable product, and identify focus areas for testing based on feedback from the production team as well as the size and skills of the testing team,” tech writer Robert L. Scheier said in his blog article “How Predictive Analytics Will Disrupt Software Development” on TechBeacon.com.

The rush to meet Big Data’s software needs is resulting in a steady flow of innovative products and unheard-of levels of collaboration. Competition is keeping prices low without sacrificing quality or function.

“As companies are becoming more interested in using their data for business analytics, software developers are scrambling to craft their response to the interest,” says product strategy manager Daniel Erickson in his MBTMag.com article “Cost-Effective Analytics Is Placing The Power Of Big Data In SMB’s Hands.”

“This is spurring more activity and creativity with the use of open-source (developed as a public collaboration and freely available), information sharing and the development of partnerships between companies,” continues Erickson, “This influx in heated competition, in turn, is driving prices down and making PA (predictive analytics) more affordable for SMBs (small to medium businesses).”

Software developers also use predictive analytics to blend two testing approaches traditionally used by programmers: shift-left and shift-right testing. Shift-left testing is done earlier in the development process, with the goal of increasing initial quality and reducing software bugs and defects. Shift-right testing monitors, tests, and constantly attempts to improve software after it is released.

“Predictive analytics is the practice of extracting useful information from data sets using statistical algorithms and machine learning in order to predict trends and behavior patterns,” data expert Sanjay Zalavadia writes in “How To Use Predictive Analytics To Optimize Software Delivery” on CIODive.com, “When applied to software testing, predictive analytics makes it easier to identify what to test and to predict quality issues before and after they occur in production.”

Data analytics and software development are slowly but steadily becoming more intertwined with each other as analytics capabilities and software demands mature. The cycle of development and testing, analyzing and incorporating analytics insights makes now the perfect time to be employed as a programmer.

Article source: https://online.maryville.edu/blog/where-big-data-and-software-development-collide/