Systems | Development | Analytics | API | Testing

Imagine getting into your car to head to work on a hot day. Your car already knows and sets the temperature, the ambient lighting, and the music you prefer. Not only that, it optimizes your route, and with Level 3 autonomy, it can even drive you there. But what does the automotive industry have to do on the backend in order to achieve this kind of personalization?

The dynamic landscape of product development continuously increases the scope of what is demanded from quality engineering (QE) professionals. So, it is not surprising that workplace learning and upskilling are critical to the organizational success, as highlighted by Workplace Learning Report: Without a doubt, the more technical side of the skill spectrum - e.g.

The open data lakehouse is quickly becoming the standard architecture for unified multifunction analytics on large volumes of data. It combines the flexibility and scalability of data lake storage with the data analytics, data governance, and data management functionality of the data warehouse.

Complexity is increasing exponentially. Compared to just 20 years ago, our personal lives and the way organizations do business has changed completely. This translates into increasingly complex and interrelated business processes and a sprawl of IT systems that can only function when each of its components runs flawlessly. Navigating this business/IT complexity can only be overcome by combining business process solutions, quality assurance tools and human ingenuity and expertise.

Banking automation has become a key driver of efficiency and innovation in the financial services industry. As the banking sector strives to meet the demands of customers while managing costs, they’re turning to technology to streamline operations. But what exactly is banking automation, and how does it work? Let’s break it down. Banking automation refers to the use of technology by financial institutions to perform tasks that would traditionally require human intervention.

For iPhone and iPad app development, one of the main aspects is the app’s performance. Performance is about your application not crashing, but also how quickly and smoothly it can carry out its functions when users interact with it. An application whose functions consume a lot of battery life, or an application that doesn’t like to wait too long until it finishes whatever it wants to do, can lead to users uninstalling the app.

As a serverless, managed service, the new BigQuery Engine for Apache Flink helps organizations bring real-time streaming data to Google AI services.

Support for conversational data agents with Gemini and enhancements to BigQuery provide a unified data platform for connecting all your data to AI.

Built on BigQuery, Dataplex provides data governance with automated data discovery, curation and management at scale.

In today’s fast-paced digital environment, delivering value quickly is critical to staying competitive and gaining buy-in from both stakeholders and customers. One of the key enablers of this rapid value delivery is embedded analytics, a business solution that allows you to integrate interactive data visualizations and business intelligence (BI) features directly into your existing applications.