We are simplifying code signing on Bitrise. Now there are two ways to automate code signing on Bitrise: using Xcode Build/Archive Steps (with iOS Auto Provision Steps merged into them) and not using these Steps, but 'Manage iOS Code Signing' instead. In both cases, we've reduced the number of things that could go wrong. Let's see what has changed!
It’s that time of year when we turn our attention to what lies ahead in the new year and beyond. For software development teams, it helps to understand the latest trends in technology, user behavior, and the broader market, as well as how to incorporate them into your mobile app development and testing strategies. This blog post discusses the mobile application testing trends for 2022 that software development teams need to know.
Let’s boldly go where no one has gone before. Get ready, Star Trek fans! Jean-Luc Picard will be representing our microservice. Once we have Jean-Luc in our ship (microservice in production), what happens on day 2? We still need to add authorization, load balancing, rate limiting, etc. With an API gateway, like Kong Gateway, you don’t have to know how to do this because a set of program components, called plugins, allow you to implement this without any problem.
Today we’re excited to announce the public beta launch of Continual, the first operational AI platform built specifically for modern data teams and the modern data stack. We’re also announcing our $4M Series Seed, led by Amplify Partners, and joined by Illuminate Ventures, Wayfinder, DCF, and Essence, as well as new partnerships with Snowflake and dbt Labs.
For decades, the technologies and systems that deliver analytics have undergone massive change. What hasn’t changed, however, is the goal: using data-driven insights to drive actions. Insight to action has been a consistent vision for the industry. Everyone from data practitioners to technology developers have sought this elusive goal, but as Chief Data Strategy Officer Cindi Howson points out, it has remained unfulfilled — until now.
Data teams across companies have continuous challenges of consolidating data, processing it and making it useful. They deal with challenges such as a mixture of multiple ETL jobs, long ETL windows capacity-bound on-premise data warehouses and ever-increasing demands from users. They also need to make sure that the downstream requirements of ML, reporting and analytics are met with the data processing.
