#enterprise

NextJS is an excellent full stack framework. Contentful is a well-known flexible headless CMS. Together are a great match, but when we talk large scale projects the challenges are completely different than the ones you may face in a small to medium scale project. Leonidas will try to raise your awareness on such challenges based on Greece's experience on redesigning Vodafone's site to create beautiful self-serve and guided journeys for Vodafone customers.

I'll introduce the concept of a data mesh and why people increasingly are gravitating towards that as a solution to their data (both online and analytical) and application modernization problems. I'll talk about the key requirements around 1) domain-oriented decentralized data ownership and architecture, 2) data as a product, 3) self-serve data infrastructure as a platform, and 4) federated computational governance (ref). And then I'll talk about how GraphQL opens up an opportunity for laying the foundation for a data mesh and best-practices in building, operating and maintaining an enterprise grade data mesh.

Did you know that 50% of users will NOT use the English version of your application, if it supports multiple languages? For ambitious high-growth businesses, going global is not "if" but "when" so it's important to recognize the importance of localization, especially in large multi-team projects. This discussion will explore the depths of localization in Agile companies and what can be achieved with a Continuous Localization system. I'll share some common issues we faced with multi-language products and how localization automation helped us to scale the process across many product teams within the company.

In 2020, Rangle partnered with the Survey Monkey team to migrate a legacy codebase to React. Survey Monkey’s best-in-class digital products were being held back fragmentation and complexity, which created a lot of rework and wasted effort for their engineering teams. Working together, we implemented a number of process and architecture changes that cut the complexity and improved workflows, letting our blended team deliver results with speed and consistently, even early in the engagement. These were not one-size-fits-all solutions, but solves that were unique and fitted to the needs of the engineering and product teams. The success of the project was due to Survey Monkey’s motivated teams that were: 1) Ready to embrace change; 2) Able to keep a firm focus on the outcomes; and 3) Readily understood the complexity of the project.
This allowed us to co-create some non-intuitive solutions that engineers at similar enterprise-level companies should know about.