Systems | Development | Analytics | API | Testing

API rate limiting involves using a mechanism to control the number of requests an API can handle in a certain time period. It's a key process for ensuring that APIs perform to a high level, provide a quality user experience, and protect the system from overload.

REST and SOAP are two critical web services technologies that revolutionize how businesses use APIs. Since APIs are so common, it's important to test them regularly to find vulnerabilities or potential operating problems within the web service. To test a SOAP or REST service, you can use tools like SoapUI or Postman to send requests to the service endpoint, validate responses, and verify that the service behaves as expected under various conditions.

Like many other industries, fintech has also been witnessing the integration of cognitive technologies as a key driving force behind recent transformations. Powerful tools, such as artificial intelligence (AI) and machine learning, are empowering businesses with swift transaction processing, precise data analysis, and optimized resource utilization. Thus, it is apt to say that a future with sustainable fintech software development cannot be mutually exclusive to the aspirations of cognitive technologies.

To address quality concerns, crucial for customer satisfaction and loyalty, businesses are investing in more scalable and efficient software testing services. In fact report by Marketsandmarkets estimated the automation testing market to be worth more than USD 28 billion in 2023. This promising growth is owed to many managed testing services providers like Zymr that are helping businesses across industries to adopt Agile/DevOps, develop complex software systems, and achieve faster time-to-market.

Significant API adoption, management, and integration with emerging technologies like Generative AI underscore the crucial role of API in modern digital landscapes. Recent research shows that companies integrating APIs experienced a significant 12.9% surge in market capitalization. Moving a step further, Gartner predicts that by 2026, more than 80% of enterprises will have employed Generative AI APIs or implemented applications enabled by Generative AI.

When you run your Go programs or integration tests, numbers of raw coverage files are typically generated and dumped into a directory specified by the GOCOVERDIR environment variable. These files contain valuable data about which parts of your code were executed during tests, offering a glimpse into your code’s effectiveness and robustness. However, sifting through these raw files to extract actionable insights can be daunting and unclear for many developers.

Legacy systems have been important in the growth and success of several organizations. However, as these systems and applications age, they slow down and become increasingly costly to operate and manage. As per the U.S. Government Accountability Office, operating and maintaining legacy systems consumes over 80% of IT expenses. Organizations must scale and update these systems to accommodate their evolving business needs. This is where legacy application modernization can help.

A robust, modern data platform is the starting point for your organization’s data and analytics vision. At first, you may use your modern data platform as a single source of truth to realize operational gains — but you can realize far greater benefits by adding additional use cases. In this blog, we offer guidance for leveraging Snowflake’s capabilities around data and AI to build apps and unlock innovation.

Instead of writing the same automation code repeatedly for the same actions, you can create keywords for those actions. When writing tests, you only need to call these keywords. This approach, known as creating a keyword-driven framework, makes test scripts more readable, maintainable, and efficient by avoiding redundancy.