The ever-growing threat landscape of hackers, cyberattacks, and data breaches makes data security a top priority, especially when integrating analytics capabilities directly into customer-facing applications. To make informed decisions, it’s crucial to understand how embedded analytics platforms function from a security standpoint.

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.

Embedding analytics into your application? New browser restrictions are on the way for Google Chrome that can block some third-party content that uses cookies from being accessible to users by default, in addition to restrictions in Safari and Firefox you may have already seen.

Not long ago, I was handed a problem that’s no stranger to the world of programming: making asynchronous threads communicate effectively within the same process. Given the widespread nature of this issue, I expected to find an existing solution to resolve it. My search led me to the concept of message queue, which seemed promising for streamlining this communication challenge.

Today, we’re excited to share that Serverless GPUs are available for all your AI inference needs directly through the Koyeb platform! We're starting with GPU Instances designed to support AI inference workloads including both heavy generative AI models and lighter computer vision models. These GPUs provide up to 48GB of vRAM, 733 TFLOPS and 900GB/s of memory bandwidth to support large models including LLMs and text-to-image models.

Application programming interfaces (APIs) have quickly become vital to modern businesses. APIs work by allowing different systems to talk to each other and share data. The commercial use of APIs and web APIs has skyrocketed over the last decades in accordance with the rise in app development, mobile apps, and e-commerce. However, how do you estimate API development time? Well, API creation and the development time frame will always vary from project to project.

API (application programming interface) document works as a developer’s compass for navigating complex services. In this guide, we provide straightforward insights into crafting excellent API documentation. At the end of this article, you will know how to succeed as both creators and consumers of APIs through effective documentation.

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.

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.

An API (Application Programming Interface) is a set of rules and protocols that let software programs communicate with each other. It acts as an intermediary between different software or services. For example, when using a mobile app to book a hotel room, the API allows the app to communicate with the hotel’s booking system. While many REST API developers have their way of doing things, some factors in API development are generally considered essential.