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.

It’s no secret that more and more organizations are turning to solutions that can provide benefits of real time data to become more personalized and customer-centric, as well as make better business decisions.

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.

Generative AI in manufacturing refers to using advanced AI algorithms and generative models to optimize various aspects of the production process. This technology enables manufacturers to create innovative product designs, streamline workflows, predict maintenance needs, and boost efficiency in frontline operations. By integrating generative AI, manufacturers can enhance decision-making, collaboration, and data insights, ultimately improving overall performance.

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.

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.

It’s a vague, unhelpful message that feels more like a riddle than an error. You know something doesn’t belong, but you're left guessing what that "token" is and why it's unexpected. Let's demystify what’s really going on behind the scenes and get your code running smoothly again. The "SyntaxError: Unexpected token" in JavaScript occurs when the code contains a character or symbol that the JavaScript engine does not expect, often due to a typo or syntax mistake.

After a short break, we’re back with Part 2 of this series on Spring Framework, Confluent Cloud, and the Kotlin language. Many organizations that write applications and microservices for the JVM have chosen Spring Framework, leveraging the many libraries available for features such as REST services, persisting data to a variety of datastores, and integration with messaging. These organizations have existing investments in building, testing, deploying, and monitoring applications using Spring.

A partnership between Google Cloud and Gretel lets data engineers generate synthetic data within BigQuery.

BigQuery’s pipe syntax introduces an intuitive, top-down syntax for understanding data transformations, and is used in Cloud Logging Log Analytics.