Low Code DevOps Opportunities for Data Scientists & Developers

Low code development has helped dramatically with the process of creating new software for data scientists.
devops options for data-driven software
Shutterstock Photo License - By Ashalatha

Data engineers and data scientists are focused on developing new applications to meet their goals. There are a lot of great software applications that can be used for a variety of data science objectives.

Unfortunately, developing software that was capable of handling big data challenges has been rather complex. The good news is that new advances in big data have helped streamline the development process. They can also create software for big data applications without a ton of unnecessary code.

Low Code Approach to Developing Big Data Software

With the advent of technology, numerous additions have been made to the digital world, one of which is software. Software and application is a set of code that is executed and helps in performing web-based or computer-based activities.

The role of software has evolved in response to changes in big data. To develop software and applications which can perform millions of tasks in an age where new data methodologies are needed, a programmer with the proper knowledge and highly qualified skills is required. At least that is what the generally accepted notion was until James Martin in 1982 published his book Application Development without Programmers. Although this book was written before big data became a household name, its principles are still applicable in the big data era.

Over time software companies came up with new Computer-assisted software tools and application development tools which fastened the process of application development by reducing the number of manual codes and using existing codes, which is more important than ever as more stringent data processing requirements are needed.. This gradually led to low-level and Low code development, which is often misinterpreted as no-code programming but is way different.

Benefits of low code application development for data science: –

A low code development platform is a platform that provides required input, output, business ideas, logic and the graphical tools and existing code needed for developing an application. In a low code development environment, manual coding is lessened compared to traditional code development and the existing code is reused to speed up the development process. It is considered a visual approach to software development in which the application functioning is in the control of the organization, and various solutions can be channelized to meet the business organization’s needs. This shuns the traditional cumbersome and complicated practice of code development by the programmers and enables the use of drag and drop system to use existing and secondary code, but functions as good as the traditional software in aspects of developing web-based apps mobile-based apps, and IoT enabled apps. As per the latest predictions, by the year 2024, more than 60% of the application will be developed in a low code environment.

Why was the low code concept developed?

Low code software development is extremely necessary and has hence been initiated. With the rising story of the software industry, the demand for software has significantly increased, which has resulted in increasing demand for software developers and programmers, and the supply ratio has been very low due to the shortage of developers and technical staff. According to the surveys, almost all employers find it difficult to hire a technical team and have to hire not such a qualified employee for who the development of manual code is a very challenging task. Hence, they need something which is just as easy as drag and drop, and hence the low code software development serves the purpose evenly. In this case, the technical team can streamline the process with big data technology by reusing the codes and create applications effectively and efficiently and save money as we know “time is money”, simultaneously taking the edge off the supply and demand controversies.

Advantages of Low code development: –

  1. Frequent prototype- The prototypes are easily available and provided as the reuse of existing codes speeds up the process. An organisation wants to save on time and money and want a faster response.
  2. Reduced Cost- The reusing of existing codes eliminates the need of writing manual code and thus saves on time which is equivalent to money. Also, it reduces the hiring of too many otherwise expensive IT staff.
  3. Security provision- The security is of utmost concern and hence remains uncompromised as all the safety tools, authenticated systems, encrypted network and secured user apps. Data is pre-feed in the low code development.
  4. Customer Experience- Low code software development provides a supreme consumer experience. Its fast and efficient system helps develop the software quickly and is flexible enough to adjust to demand changes and market trends. Therefore, it provides an updated app that sets new trends.
  5. Digital update- The flourishing businesses and the recent trends in this world where everyone tries to top the frantic rat race, the fast is the new success mantra, and thus, for fast processing, automation is a must. The low code industry provides automation, fast and efficient service at a moderate cost.

Demerits of low code development: –

  • The first and foremost problem with the low code platform is that drag and drop provides access to limited functionality codes, almost basic to all applications. But the unique features needed for an app to stand out and be out of the box requires manual coding, which generally is tough again.
  • An employee with absolutely zero ideas can’t be the user of these apps because for the selection of the right code and effective implementation and to complete an app, a professional with the necessary skill is required.
  • There is also a risk of low-quality apps being developed.

Low Code Software Development is Crucial for Data Scientists

Data scientists continually need to rely on more sophisticated software to achieve their goals. However, this doesn’t mean they need to commit to unnecessary development cycles when repurposing existing code or minimizing the need for code altogether could be possible with data-driven development methods.

There is usually a big confusion between low code and no-code development for data science, and both are often considered the same, but they are very much different. The No-code platform is one that requires no coding at all, no professionals, only citizen developers, and is usually faster. But low code development involves a little bit of use of manual coding and visual modelling tools and out of the box functionality to serve as the cherry on top.