Solving Database Re-platforming Challenge with Adaptive Data Virtualization
Many organizations are exploiting the advantages of cloud computing. However, doing so often comes with a high cost if they choose to use the conventional data migration approach. They face challenges of costs, timing, and scalability. These problems can discourage some of these organizations from migrating to cloud computing services. <!--more--> Adaptive Data Virtualization (ADV) removes that barrier and allows companies to transition to cloud computing. It facilitates quick migration of services to cloud data warehouses at low costs.
ADV's approach is suitable for corporate firms seeking to adopt cloud services quickly without facing financial and time risks.
This article describes what database re-platforming is and highlights the issue of application re-writing when it comes to database migrations. It also discusses how adaptive data virtualization can solve the challenges associated with database re-platforming.
What does database re-platforming entail?
Re-platforming refers to slightly modifying applications to take advantage of new cloud infrastructure. One of the changes is modifying how an application interacts with the database to utilize automation services. Ultimately, the changes enhance an application's functionality, scalability, user experience, and promotes a company's profitability.
Re-platforming is a quick way of changing an application to fulfill the needs of an organization. An alternative would be re-architecting it, which is more costly and time-consuming. A developer requires less specialized expertise during re-platforming due to the availability of "plug and play" third-part SaaS components. This approach allows you to focus on a specific area to improve an application while maintaining the rest of its intact and functional.
The problem of application re-writing in database migrations
It is time-consuming
Writing an application from scratch can take several months to complete. It can take up to two years for the big firms because of the massive data flow they experience.
When re-writing a business application, there must be someone who interprets the logic behind the existing app. The individual must then create new code while factoring in the current and future functions of the organization. The interpretation also considers the database structure and data and their translation.
Once developers create a functional code, they face the next challenge of ensuring that hardware and operating systems are congruent for this application. The application will take an extended time at the end of its development due to extensive and lengthy tests before becoming available for use. In worse-case scenarios, slight errors in the code can force developers to restart the process.
The issue of bugs
Re-writing an application comes with the challenge of dealing with bugs. Any developer experiences this problem when writing code because of errors that arise during the process.
Unfortunately, some bugs are challenging to reproduce, and it could take several weeks or months to identify them. For this reason, a developer has to hold back on their progress to fix these errors. Having bugs during re-writing code lengthens an application's development time.
It is expensive
The cost of re-writing an application is often remarkably high. That is why companies are reluctant to re-write their legacy applications even when there is a high need. These costs occur in the form of time lost to develop the application until its maturity phase and the financial costs of developing it.
Notably, re-writing legacy applications require additional IT resources and skills in coding, modern languages, and technology to meet current business demands.
Creating a mainframe re-write is a perfect example of how costly the process can be due to the costs associated with acquiring relevant infrastructure. Firms incur further costs during re-developing, re-architecting, translating, and testing activities.
It took as much as $580,378 to re-write one mainframe in 2018. Today it could be more. In context, companies like IBM with at least 12 applications running on a mainframe would incur as much as $6.96 million in 2018.
Unsatisfied customers
Companies operate on the promise that their activities will attract and retain more clients for more profits. A decision to re-write an application often aims to promote this objective. However, in some cases, re-writing is equally detrimental to an organization's customer base. During development, a company may take the existing application down. The firm will miss out on prospective customers and could lose returning clients as well.
Besides, new features introduced in the application may receive a negative reception from the customers. Even worse, if the new application experiences bugs. These bugs can make it crash or have undesired behavior that clients do not like.
If the customers feel irritated and complain about the product, your rivals could take advantage of this new niche. They can create a new application or improve theirs that capitalizes on the app's vulnerability. As a result, several loyal clients of your firm would leave for the direct competitor. Thus, what could have been construed as an intelligent decision may pose an existential risk for a business firm.
Adaptive data virtualization for seamless data visualization
Adaptive data virtualization is becoming increasingly crucial for data visualization in organizations. It focuses on managing data in a way that ensures people access it with ease. This value is also vital in data visualization, where companies display data graphically for ease of analysis.
Adaptive data virtualization increases the speed of data access
The most significant advantage that adaptive data virtualization offers to data visualization is its data combination power. It combines data from several sources by integrating several processes into one platform. It creates web integration processes that incorporate workflow, navigation, and data extraction.
After collecting this data, it becomes easier for data visualization to access it for further manipulation to create pictorial illustrations.
Adaptive data visualization provides data visualization with a more agile and less resource-intensive way of accessing data. It becomes easier to analyze data because of the centralization of its location.
Adaptive Data virtualization offers a variety of data from one source
Since adaptive data visualization combines data, it becomes easier for several experts and non-experts to access information fitting their goals.
For instance, in a database of organizations collecting information on the Ebola Virus epidemic in West Africa, several experts would be interested in the data. Some will need it to determine the extent of the current spread or racking purposes. Others will need data to predict future occurrences of the disease, and others will need data on the number of strains of the virus.
All this data is slightly different, and specific people will be handling it. Such is the case that every firm deals with varying forms of data. Thus, having an adaptive data virtualization system would enhance its visualization. It would allow people to access several forms of data and enhance their analysis.
Adaptive data virtualization vs. conventional migration
Migration to the cloud
Adaptive data virtualization has become more superior to conventional data migration systems owing to its advanced features. Adaptive data virtualization facilitates re-platforming by allowing applications to move to the cloud as they are, without needing instant modifications. In contrast, conventional migration often entails cumbersome re-writing and re-architecting of applications.
Adaptive data virtualization faces fewer challenges when re-platforming because applications do not need modification. Thus, it is faster to perform cloud migrations than using the traditional approach.
Room for change
A conventional migration approach requires a developer to alter the dynamics of an application and fit it with cloud-compatible features. Upon its incorporation into the cloud, minimal or hardly any changes are possible. However, adaptive data visualization guarantees a modernized approach that allows one to modify the applications while still on the cloud.
Associated risks
Because of the risky nature of re-writing code and re-architecting applications, the traditional approach could jeopardize an organization's smooth transitioning to cloud services. Adaptive data virtualization is a safer option because it allows for the bypassing of the re-writing process.
Cost
Conventional migration is more costly compared to adaptive data virtualization because it entails hiring experts to re-write code and acquiring new infrastructure for re-architecting. Adaptive data virtualization requires none of that; hence its low costs.
Conclusion
Conventional migration to the cloud is a complex process. Re-writing code from scratch to create a functional application is time-consuming. It is also precarious for any business model because the costs could jeopardize its profitability. A company undertaking this process risks losing its clients due to a delayed release of a functional application.
Adaptive visualization takes advantage of the traditional approach's weaknesses. It enhances the speed of re-platforming while lowering the costs of doing so. It promotes the re-platforming of applications without necessarily modifying them.
Futher reading
- An Overview of Database Testing
- Data Visualization With Tables in Reac
- How to Migrate an On-premises Application to Serverless
Peer Review Contributions by: Onesmus Mbaabu
