Systems | Development | Analytics | API | Testing

3 Self-Service Analytics Use Cases

Data democratization can feel like just another business buzzword. But beyond the buzz, it represents an important concept: when your employees lack access to the data they need for decision-making, decisions will stall and your business will suffer. Getting value from your data—and delivering that across your organization—is a persistent challenge. Getting value from your data—and delivering that across your organization—is a persistent challenge.

A comprehensive guide to handling dates and times in PHP

From accurately tracking the opening and closing of financial markets to preserving the history of posts and research papers by properly saving the times they were created, edited, and deleted, software engineers must understand how to handle dates and times in their applications. The concept of dates and times is especially important in programming. All programming languages enable developers to manage dates and times in their applications.

The Industrial Metaverse: What Is It and How Will it Shape the Fourth Industrial Revolution?

The industrial metaverse might seem like something from an advanced, sci-fi universe far in the future, but the building blocks of this concept are already present and functioning today. The early stages of a digital ecosystem, which will blend the real and virtual worlds, can already been seen across industries like architecture, transportation, and manufacturing.

Implementing GraphQL Subgraphs with Ballerina Swan Lake for Federated APIs

This article is based on Ballerina Swan Lake Update 7.2. Today, I want to share insights into implementing GraphQL subgraphs in Ballerina. But before we dive into coding, let's take a moment to understand GraphQL federation and the problems it solves. Ballerina Swan Lake WSO2 is an open-source and cloud-native programming language optimized for integration GraphQL Federation is a way to build a unified GraphQL API by combining multiple GraphQL services.

How to Build a Smart GenAI Call Center App

Building a smart call center app based on generative AI is a promising solution for improving the customer experience and call center efficiency. But developing this app requires overcoming challenges like scalability, costs and audio quality. By building and orchestrating an ML pipeline with MLRun, which includes steps like transcription, masking PII and analysis, data science teams can use LLMs to analyze audio calls from their call centers. In this blog post, we explain how.

Leveraging Kotlin Collections in Android Development

Kotlin has gradually replaced Java as the lingua franca of Android programming. It’s a more concise language than Java, meaning your code works harder and you can build leaner applications. And Kotlin Collections are fundamental. These collections play a fundamental role in our work as programmers by simplifying the organization and management of data. Whether it’s a list, set, map or other data structure, they allow us to categorize and store data logically.