Every API product manager wants as many developers as possible adopting and using their APIs. They want them to get to Hello World quickly and have a great developer experience (DX) along the way. Of course, the bigger goal is to be able to tie API success into the larger objectives of the company. For many, despite the best intentions, their metrics are too simplistic, narrow, and based on outdated models of engagement.
For years now, enterprises have embarked on their own unique digital transformation journeys. They’ve worked to evolve processes and methodologies, to securely move applications and systems to the cloud, and to speed up cycles to deliver more, faster, and win in their markets. But 2020 was a pivotal year. The pace of change went on fast forward. Suddenly, teams were even more distributed than ever before. Flexibility became an imperative. Remote collaboration was required.
While there has been some incremental improvements in the last few years, there has been nothing significant recently - and I think there are four clear reasons for that. Firstly, there has been a lot of consolidation in the industry recently. When that happens, behemoth vendors focus far more on selling than building new products that are going to disrupt the industry.
Artificial Intelligence is in the news a lot, and it’s hyped as a cure for all ills in the same breath it’s suspected of spelling doom for us all. What’s the truth behind all the noise? What does artificial intelligence do, seeing as it is simply everywhere. The truth of the matter is that whatever the would-be prophets say, artificial intelligence and machine learning is here, now, and has applications to your day-to-day.
API gateways provide a central point to govern and control access to your APIs, enabling customers and partners to quickly create new experiences. Amazon API Gateway has native support for a variety of compute resources like AWS Lambda or Amazon Elastic Compute Cloud (Amazon EC2).
Most of the applications being developed today have complex requirements. Hence, they need to be tested thoroughly and for each use-case, before they are ready to be deployed in the production server. We all know how important it is to perform unit testing on separate modules or “units of codes”.
Avoiding a common pitfall in data science by enabling history mode.
Use schemas to make life easier for your analysts and engineers.
