How To Build Big Data Applications With A Low-Code Platform
When people think of low-code platforms, they often think that they are only good for building simple applications. They believe that a developer with programming experience will not be able to create business applications. And that developers and technology professionals only use low-code to create simple applications, such as forms or simple procedures.
But really, that is not so. The low-code platforms can create complete and powerful applications that can handle large amounts of data, what is popularly referred to as Big Data. The processing of large volumes of data is an upward trend that is having important applications in many sectors, being one of the pillars of the so-called Industry 4.0.
This post is inspired by this other one published: 6 steps to use low-code platforms to create Big Data apps. The article breaks the myth that low-code does not allow building complete applications, offering several good tips on how we can use low-code to create applications that can take advantage of the full power of Big Data.
The article points out that low-code platforms can have some problems when handling massive amounts of data. He points out that: “low-code is designed to work with transactional data and defined record sizes.” This feature seems to rule out the possibility of dealing with Big Data. Still, the article goes on to say.
There are ways to use low-code for large amounts of data if there is enough business value to justify the development of a methodology that facilitates it. Since development with low-code must work with fixed registers, the main task is to format these data to adapt them to the format of the available registers.
Six Steps To Creating An Application That Manages Large Amounts Of Data With A Low-Code Platform
- According to the cited article, the first step to do this is to define the company’s requirements. Determine what business problems the application will solve and the types of Big Data used to solve them.
- Then he talks about the use of Artificial Intelligence for the processing and elimination of unnecessary data to keep only what is necessary. Indeed we can design an algorithm of machine learning to detect and eliminate unnecessary data. Still, in many cases, it is not necessary such complexity and through a processing core of the data may be more than enough in many cases for that task detection and elimination of unnecessary data.
- And once we have only the necessary data, we must format these appropriately to store each one in the corresponding field.
- Next, we will have to create the necessary APIs to access these fields containing the filtered, processed, and formatted data.
- At this point, it will be time to make use of an ETL (“extract-transform-load”) tool to normalize and transfer the data sets to other systems, allowing compatibility with these.
- Finally, we can put together all the pieces of the application through the low-code platform and test if it is receiving the data correctly, processes it as it should, and returns the desired results, allowing us to perfect the processes until reaching the necessary result.
What Is The Best Low-Code Platform To Create Big Data Applications?
The first thing is to remind you that we publish a complete guide to choosing a low-code development platform, which is the most developed answer to this question. A more concrete answer would be that it depends. It depends on the purpose and the needs. We do not think that there is a better platform than the rest for any area.
Now, suppose we focus on the professional development of applications for the business management field that use large amounts of data. In that case, it is a great option since it has an integrated database and visual programming that allow us to define and shape data in an agile way, a powerful API that allows the platform to be open to various standards, as well as an integration with Web Assembly that add a simple and powerful deployment to any system.
Several of our clients manage large volumes of data in real-time, including their insertion, modification, elimination, and filtering, in critical business infrastructures, such as hospitals or medical centers. This is the best example that is a platform prepared to face this type of architecture.