Have you ever used an app on your mobile device and experienced slow load times, crashes, freezing screens, and privacy breaches? Or has your team discovered an error in an existing functionality weeks after you’ve launched an app? If you are a mobile app user, the answer is likely yes. As a consumer, you may have been triggered to delete the app completely, reinstall it, or download a competitor’s app.
In the fourth blog of our internal platform team series, Zsolt Márta explains how Bitrise uses Terraform Cloud to enable teams to manage resources in a fully self-served way.
The idea of running compute and storing data in the cloud is no longer a novel concept. With the evolution of 5G and Internet of Things (IoT), this brings along the next evolution of edge storage demands. Today, around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2025, Gartner predicts this figure will reach 75%.
Hitachi Vantara recently commissioned Forrester Consulting to conduct a Total Economic Impact (TEI) study to examine the value that customers could achieve using cloud and application modernization services from Hitachi Vantara. To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers at companies with experience using cloud and app modernization services from Hitachi Vantara.
Read our hands-on guide that explains how to build, deploy, and host Flutter Web apps on Firebase Hosting so you can reach more users in browsers with the same experience as on mobile devices.
SonarQube by SonarSource is the leading tool for continuously inspecting the code quality and security of your codebase and guiding development teams during code reviews. It is an open-source tool that has support for 29 programming languages as of the time of writing this article, and the number is growing. SonarQube’s key features include.
There are a variety of technology stacks for Artificial intelligence (AI), Machine learning (ML) and data analytics applications. However, the ideal programming language for AI must be powerful, scalable and readable. All three conditions are met by the Python programming language. With outstanding libraries, tools and frameworks for AI, ML and data analytics, Python has proven success leveraging all three technologies.
Sharing your application with real users in real environments before your app goes live has to be one of the most exciting stages of app development. You get to release your application to targeted users to test and uncover bugs in your app — in other words, to beta test it. Speed and automation are critical factors when releasing your app to beta testers.
Artificial intelligence (AI) has been a focus for research for decades, but has only recently become truly viable. The availability and maturity of automated data collection and analysis systems is making it possible for businesses to implement AI across their entire operations to boost efficiency and agility. AI has the potential to transform operations by improving three fundamental business requirements: process automation, decision-making based on data insights, and customer interaction.
