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.

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.

By combining speech-to-text with BigQuery's analytics capabilities, you can understand things like customer sentiment and recurring product issues.

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.

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.

Maintaining standards in an ever-growing codebase that multiple developers contribute to can be difficult and tedious. Ensuring that the codebase follows best practices and does not deviate from the standards is essential for any project. But this is typically something that can only be enforced manually by code reviews and other similar processes. As with any other manual task, this can be time-consuming, tedious, and error-prone. That's where architecture testing comes in.

The java.lang.IndexOutOfBoundsException in Java is thrown when an index used in arrays, lists, or strings is not valid. A valid index must be a number from 0 up to one less than the total number of items. For example, in a list of 3 items, the valid indices are 0, 1, and 2. Here’s a quick example: The error message you’d get would look something like this: Here are some other common ways the IndexOutOfBoundsException might be thrown.