The service mesh architecture pattern has become a de facto standard for microservices-based projects. In fact, from the mesh standpoint, not just microservices but all components of an application should be under its control, including databases, event processing services, etc.

Data aggregation is the process of combining and summarizing data from disparate sources into a cohesive dataset. It prepares data for analysis, making it easier to obtain insights into patterns and insights that aren’t observable in isolated data points. Once aggregated, data is generally stored in a data warehouse. Then, you can leverage it to gain a holistic perspective on your operations and market trends, design effective risk management practices, and make more informed decisions overall.

Learn why SAP data is the critical ingredient for manufacturing organizations to move from reactive to predictive analytics.

Time series data is everywhere. It captures how systems, behaviors and processes change over time. Enterprises across industries, such as Internet of Things (IoT), financial services, manufacturing and more, use this data to drive business and operational decisions. When using time series data to perform analytics and drive decisions, it’s often necessary to join several data sets.

Think about the difference between a basic and complex bug. A basic bug might be a typo in a button label – it's obvious and easy to fix. Complex bugs, though, are trickier. They might only happen under specific conditions, involve multiple parts of the software, or cause confusing symptoms. For these complex issues, a standard bug report isn't enough. Developers need more clues and context to pinpoint the problem. That's where an advanced bug report template comes in.

A part of Gemini in BigQuery, data insights leverages table metadata and profiling information from Dataplex to surface relevant, executable queries.

Like many tech endeavors, QA has undergone radical evolution over the past decade, and to keep up with the competition, dev teams are looking toward the future of QA and test automation. Since machines can do low-skill testing, dev teams are looking for expertise and complex skill sets, which means that human testers’ day-to-day responsibilities are changing thanks to automation. In this article, we will discuss test automation, what it is, examples, types of test automation, and uses.

There's no doubt that, as a technology provider/integrator, you're likely seeing many customers across all segments looking to advanced analytics and artificial intelligence for optimizing their growth. Given the vast volume of data that these innovations consume or create, it's clear to see the importance of being able to offer your customers reliable, secure, sustainable and scalable data infrastructure solutions.

In the video game development industry, releasing a well-polished and critically acclaimed title is crucial to success. However, even experienced development teams may face unexpected challenges in identifying and resolving issues before the official release. This is why beta testing is so important; it helps developers catch and fix any remaining problems before the game is released to the public.