Developer Satisfaction Tracker
In our DBaaS Developer Satisfaction Tracker, we benchmark 15 DBaaS offerings and provide deep insights from 1,856 DBaaS users. Our analysis measures usage and awareness for each offering and how they perform across 14 different attributes. We measure how satisfied developers are with each offering based on these criteria. We also look at how these attributes drive adoption/rejection decisions and likelihood to recommend a DBaaS. Through our developer profiling analysis, we look at a wide variety of criteria to identify what attributes have the most influence on adoption, satisfaction, usage, and recommendation patterns.
The digital games market is growing quickly and the rise of AR/VR technology and cloud based games is changing the experience. Developers need tools such as game engines to simplify and expedite the development process and meet growing demand. We benchmark 13 game engines, including Unity, Unreal, and Amazon Lumberyard among others, and provide deep insights from 1,938 game engine users. Our analysis measures usage and awareness for each vendor and how they perform across 13 different attributes. We measure how satisfied developers are with each vendor based on these criteria.
We’ve measured the experiences of 1470 developers using Cloud IaaS, to provide deep insights on what drives developer decisions, the Cloud IaaS market gaps, the greenfield opportunities and the profiles of your competitors’ developers.
IoT Cloud Platforms
Based on the experiences of 880+ IoT developers who used IoT cloud platforms in the last year, we provide deep insights on the strengths and weaknesses of vendor offerings, the market gaps given what developers are after, and the profiles of those who use, are satisfied with and recommend the top vendors. This report is based on the responses of IoT developers in our November-December 2016 Developer Economics survey.
In our Machine Learning Platforms Developer Satisfaction Tracker we benchmark 14 leading ML platforms based on the responses of 2,300+ data scientists and machine learning developers. We provide deep insights in the strengths and weaknesses of vendor offerings based not on analyst opinions but rather on developer satisfaction across 18 platform attributes. We also look into recommendation patterns, discuss market gaps given what developers are after and also profile the groups that use versus not use, are satisfied versus not satisfied and recommend versus not recommend each of the top ML platforms.
Containers have been used to improve application deployment and data centre operations by some cloud industry leaders internally for many years. The release of the open source Docker project in 2013 was the trigger for much broader interest. The container looks set to become an extremely popular abstraction for managing application deployment and scaling, possibly replacing the virtual machine as the most popular of all. Several Container-as-a-Service (CaaS) vendors have emerged offering to handle various aspects of container operations, leaving developers to focus on building their apps.