The first stage of development
We've started earlier this year with an idea on a piece of paper (well, digitally anyway 😉 ). Since then, our core team has been assembled, working remotely with each other every day. We've planned our work with a sense of urgency from day one, even if we are making sure we build the right technical foundations of the project in order to be able to build upon it, and enable innovative features in the future.
We've decided to have our platform support rFactor 2. There are several reasons why, one of them being we just love it! The fact is rFactor 2 is an amazing racing simulation software. Its focus on simulating reality and its physics well is something dear to us, and aligned with what we were looking for. It also has an open structure at its core, making it is possible to get a lot of relevant information about configurations and telemetry data in a relatively easy way. Furthermore, resources continue to be put into its development, and we are of the opinion that Studio 397 has been doing a great job since they took over development in late 2016. Our expectation is that this trend will continue in the future.
One of the key initial steps in our project is to create a robust infrastructure to handle data end-to-end, from local computers to our private cloud servers. This is the focus of our first milestone. To that point, rFactor 2 makes available a significant amount of telemetry data, which is a good thing, although it creates a technology challenge. Not keen on short-term shortcuts, we have forced ourselves to decide on a good technology stack, optimized for our solution, which concerns big volume of data flows and processing, so that we can provide users with the benefits of having all this in one place. Recent developments in this field also came to our rescue, as they made it much easier to create flexible data streams and process all this big data in a timely way. As it's always the case, we have already run into some unanticipated challenges in our endeavor. For example, we had to go through an important learning curve regarding the different existing cars and tracks' packaging structures, in order to correctly identify them in various situations. This understanding enables simple but important features such as easily organize, search and look at data for a particular subset. For example, we might not only want to see data from a particular vehicle, but from the latest version of it as well. Obstacles like these meant we took more time than expected in parts of the development. However, as we get to know rFactor 2's ins and outs better, we start having a better sense of the development pace needed to push forward.
The last few months have been very exciting, and we can't wait to share with you the output of our work at this stage. We are now estimating it should be coming in the next few weeks.