Continuous Testing - Preparing Integration Data

Introduction Continuous Testing – Preparing Integration Data

Continuous Testing

Like all other key technology trends, DevOps is not any new to the industry with the technology needs increasing, the industry shifting towards Automation at a much lightning pace. 

DevOps is not just a technology trend, and it is more than a trend, diversification of various likely minded people who have a frequent target in mind on an industry level. It could be best defined as a cultural change that is aiming towards a collaborative process where the developers and operations teams come together to achieve a particular target, i.e. successful transformation and development of the organization. 

No doubt, there are numerous developers available all around the globe that can build a program with much ease. Still, we are neglecting at the management side of the story, by management what I mean to say is that the Testing, QA and Operations teams.

Embed quality in applications; teams add more modern testing practices in the form of small quality checks performed throughout the application pipeline.

Taken From Article, Continuous Testing in DevOps and Best Practices for Implementation

Continuous Testing in the perfect way can be explained as that type of approach where it involves a process of following diverse routes to ensure the best user-driven experience is delivered.

It is not always viable to perform all the tests every time a new feature is patched, so for this continuous testing strategy to serve a cultural change over the organization, it should be able to cultivate four crafts such as Test Early, Test Regularly, Test Rapidly and Test everywhere along with Automation.

The main aim is to evaluate the best quality at every step by bringing in together the Developers and Operations team and the QA and DevOps teams.

Integration Data

With the outburst of data both in volume and variety, and data exchange playing one of the critical roles among business processes to grow, this process is has exceeded beyond steps to send from one place to another. Businesses these days have the exact about the complexity in data. As a result, they are highly drawn towards valuable and efficiently managed data, to promote growth.

So, among those numerous sequences of steps, the first one is to preparing Integration data or Data Preparation.

Data preparation involves the process of identifying, cleansing and transforming raw data into meaningful sets of data. It also suggests adding corrections to data, reformatting and combining of data sets to enhance the quality of data that would be ready to process. 

Need for Continuous Testing – Preparing Integration Data

There is a numerous range of factors that are cultivated together to allow a business processing system to excel its influence on the market and receive more resources and collaborations to bring in more products. This is not just a linear path as the biggest problem is at the ground level of data management.

If Data was already available in a refined manner or the tools that have been built to promote agile development processes could enhance data to a level where no rectifications would be required, we would not be reading about these topics today.

As they say, it looks a lot less complicated from outside than it is inside. The same goes with data with already some trillions of un-ordered data available that is complex to scape and handle with all the inconsistencies, it is evident to group things in a particular manner to operate efficiently which brings us with a new topic called, Test Data Preparation.

 It is never easy moving from manual processes to fully automated systems as it involves a lot of serious planning and productivity. One way of doing it is by making testing an actual key element or service. Let’s understand what Test Data is and its and requirements in next.

Preparing a Test data involves test data injection and measurements at specified points and under conditions where the need and acceptable level of risk is determined. So, we can say that it should be a model that must be able to define a point of testing/monitoring as an interface and then connecting it with another interface depending on the need.

Requirements of Test Data are the Primary Variables, Field dependency, Injection Points, Data Format and Data Outputs. All these combining leverage to unifying inputs and outputs of test data for test automation.

Steps involved in Preparing Integration Data

 This is not an orthodox strictly applicable process for integration data as this might vary by industry, organization, and most importantly the need. Still, the standard ground rules are the same for almost everywhere.

  • Access or Gather Data: This is the very first step in the process of preparing integration data as finding the right Data is quite essential to carry out a further method, or this can also come from existing data.
  • Cleansing and Assessing Data: Once the Data is gathered, it is essential to cleanse the data as the manual process can’t make sure that it would be error-prone, costly and time-taking. So, it is quite necessary to make sure that the data collected is analyzed properly and is ready to process by removing unnecessary outliers, adding up missing values, and what-so-ever standards are required. Once Data is cleansed, now its time for validation by Testing for errors in the data at that step. 
  • Transforming Data: By this stage, we might have achieved a clean structural data which is ready to be converted into various types and formats that would be update consequently to the warehouses ina clear manner making it easier to be understood by the user. Enriching Data is nothing but adding more functionalities to provide deeper insights. 
  • Re-formatting Data: A nicely formatted data provides you with a good and natural representation of data too which would cut short the wastage of time and money in implementing non-required tools. Moreover, its practical use should be done right after transforming and enriching your Data is done.
  • Store and use data: Once Data is analyzed, cleansed, and enriched for processing, it has to be sent somewhere to be able to export to any common platform to make the best use of the tools one has installed in an organization. This allows you to import and export data any point from anywhere within the organization in a hassle-free way. 
  • Automating Data repeatedly: After going through all these stages of the refinery, it must be obvious to assume that the Data is almost integrated and is ready for production but what if there arises another change or any update, so to avoid that we have to keep running tests repeatedly for every single log with the help of Automation or manual steps. But using Automation might save us a lot of time.

Advantages of Continuous Testing – Preparing Integration Data

If we talk mainly about analytics, data preparation and data integration have been evolved to address so many issues of data inconsistencies quite quickly, and there is a need for it in every organization that is looking to transform their businesses. It not just only improves the productivity of the firm that’s using it. Still, it has also become a standard tool for enterprise data bringing in collaboration between talented developers, talented ops engineers, and strategic planning. Here are the few essential benefits of Preparing Integration Data. Nearly 70%–80% of the data scientists believe that the worst part of their job is data preparation. However, it is imperative to get useful, valuable and clean data to carry out efficient and accurate business decisions.

  • Fixing errors rapidly: This provides us with predicting failures previously by running repeatedly automated tests from time-to-time before processing. Once the Data is removed from its original locations, the errors increase to the difficulty in understanding them.
  • Better Business Decisions: When the Data is clean, analyzed and is properly formatted, it helps to understand the effective use of it by minimizing time and space, thus making take better business decisions, production, and deployment.
  • Collaborative usage: Using the Cloud for data preparation means it will always be active, which means it doesn’t require any technical assistance or installation again allowing various teams to work for faster results collaboratively.
  • Future with Cloud: As we know Cloud is upgrading itself automatically each day, this helps in matching pace with the industry trends so that the new patches or upgrades can be brought into use the moment it’s released letting the organization stay ahead of the innovation curve.
  • Scalability: With Cloud, the functionalities of this process increases even more as it can grow at the pace of business. The business systems don’t have to worry about the outcome or the progress.
  • To deliver top-quality data: Repetitive cleaning and reformatting data to make enrich datasets ensures that all the Data is always analyzed and is of high quality to be used anytime.

As this process has ended, it keeps revolving around a nucleus to get the best value data, and it can only be extended to various industries and technological trends. This also helps in changing the way that you do business. 

Conclusion Continuous Testing – Preparing Integration Data

Successful continuous testing is a strategic benefit, enabling companies to offer better value to their higher-performing consumers. But making the switch to continuous testing isn’t a simple feat, and if you don’t learn the fundamental tenets of it, you may find yourself heading for catastrophe.

Also, Read –  Continuous Load Testing Tools and Features



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