AI is no longer just a buzzword – it’s the driving force behind the next wave of innovation in the software industry. Companies that embrace AI today are automating tasks, boosting efficiency, and unlocking new levels of productivity. However, as revolutionary as AI is, adopting it within technical software teams isn’t without its challenges. From skill shortages to navigating ethical dilemmas, businesses face a steep adoption curve.

A year in at Confluent, Product Manager Surabhi Singh has learned a lot about data streaming—and even more about herself. In this fast-paced environment, Surabhi is highly motivated and committed to her work strategically planning, coordinating, and delivering product improvements for customers whose business operations depend on Confluent Platform.

The world of quality assurance (QA) has changed, and there’s no turning back. Digital transformation is driving the need to produce high-quality software faster, while also keeping costs under control. The old idea of choosing between speed, cost-efficiency, or quality? That’s out the window. Today, you need all three to succeed, and they’ve got to work together.

In today’s digital landscape, maintaining a smooth user experience is essential. Websites and applications are expected to perform reliably and quickly, with minimal interruptions. One effective approach to enhancing user experience is synthetic monitoring, which uses simulated transactions to test performance. This article will delve into how synthetic monitoring improves user experience and highlight its benefits, along with code examples for implementation.

In today's data-driven landscape, data analysts rely on powerful database management tools to organize, query, and extract insights from vast datasets. With a multitude of options, choosing the right tool can significantly impact efficiency and performance. Tools like MySQL Workbench, SQL Server Management Studio, and Google BigQuery provide robust platforms for relational data management, while options such as MongoDB Compass cater to unstructured data needs.

Forrester’s Global Retail E-Commerce Forecast, 2024 to 2028, predicts global online retail sales will rise from $4.4 trillion in 2023 to $6.8 trillion by 2028. Despite this increase, 76% of retail sales ($21.9 trillion) will remain offline by 2028. To remain competitive, retailers are enhancing their omnichannel strategies by seamlessly integrating online and offline experiences.

Containerized microservices are essential to cloud migration and digital transformation plans. Do you have a clear picture of what containerized microservices are and why they’re important? In this guide, you'll learn about containers, microservices, and how they work together. First up is a bit of history that explains how the need for containers came to be. Here's the key things to know about containerized microservices.

If you’re working in the data space today, you must have felt the wave of artificial intelligence (AI) innovation reshaping how we manage and access information. One of the areas affected is data catalogs, which are no longer simple tools for organizing metadata. They’ve evolved dramatically into powerful, intelligent systems capable of understanding data on a much deeper level.

Engineering data management is a key skill for data analysts who handle complex datasets across engineering systems. This field involves processes for managing, organizing, and optimizing data generated by engineering teams, such as IoT device data, operational metrics, and manufacturing details.

We strive to improve clarity and user-friendliness, and have thus focused our efforts on several features that align with this objective. Our new Sidebar Menu, the Release Tracking Feature and an increased comparison range for Releases and Markers as well as the possibility to show error messages in notifications all fall into this category. Summary.