As the wave of AI sweeps through the technology landscape, many have hopped on board. Interestingly enough, and often overlooked, is that many AI capabilities are served through APIs. Fancy user interfaces integrate with the actual mechanisms where the magic happens: the APIs. So, when generating revenue through AI platforms, the APIs drive the revenue.

In today’s digital age, citizens expect seamless interactions with government agencies, mirroring the efficiency and user-friendliness they encounter in the private sector. However, achieving exceptional citizen experience (CX) requires routine testing and close attention to software quality and performance. As agencies navigate the complexities of delivering digitized services, continuous automated testing (CAT) is a game-changer in enhancing CX while maintaining speed and reliability.

Whether you love, hate or remain indifferent towards data, it’s impossible to deny its importance in today’s business landscape. Businesses across all sectors and industries collect data and perform data analysis, to better understand their customers and business processes, in an effort to boost productivity, reduce expenditure and gain competitive advantage.

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In modern web applications, real-time communication has become more than a feature: it's gradually evolved into a necessity. Users expect instant updates, live interactions, and dynamic content. In Rails applications, Action Cable has long been the go-to solution, harnessing WebSockets to fulfill these demands. In this article, we introduce: Let's get started!

Bun and Node.js are two JavaScript runtime technologies to run JavaScript on the server. Node.js is the undisputed king of server-side development with JavaScript, but Bun has gained popularity thanks to its unbelievable performance capabilities. The real question is, though: does it really make sense to use Bun instead of Node.js? Let's learn about Bun and Node.js, dig into their characteristics, and explore some scenarios where Bun can be a better alternative to Node.js.

Let’s pose a hypothetical scenario. You're the API product owner at the Paris, Texas Regional Airport. You're in charge of two main APIs: Flights API and Scheduling API. Flights API is primarily used by local research institutions that are interested in read-only access to information about departures and arrivals. Scheduling API is primarily used by airline partners who are interested in updating information about their flights and gates.

Data integrity refers to protecting data from anything that can harm or corrupt it, whereas data quality checks if the data is helpful for its intended purpose. Data quality is a subset of data integrity. One can have accurate, consistent, and error-free data, but it is only helpful once we have the supporting information for this data. Data integrity and quality are sometimes used interchangeably in data management, but they have different implications and distinct roles in enhancing data usability.

Did you know data scientists spend around 60% of their time preprocessing data? Data preprocessing plays a critical role in enhancing the reliability and accuracy of analytics. This blog will discuss why data preprocessing is essential for making data suitable for comprehensive analysis.

Understanding how to make a GET request to an API using Python is an essential skill for developers. This article will guide you through the process, demonstrating how to use Python’s ‘requests’ library to fetch data, handle the full JSON object in response, and manage API errors efficiently. Step into the practical world of Python GET API calls without any detours.