This blog post is part three of a three-part series on how they’ve scaled their API management with Kong Gateway, the world’s most popular open source API gateway. (Here’s part 1 and part 2.) In 2019, our Kong-based API gateway platform hosted about 1,900 proxies and handled 375 million transactions per month. 2020 saw a tenfold increase in both metrics to more than 11,000 proxies and 4.5 billion transactions per month—about 150 million transactions per day.
Things don’t always go well when using an API for the first time, especially if you’re a beginner and it’s your first time integrating an API into another system. Often documentation is lacking in terms of errors, since it’s easier to anticipate things going right, than things going wrong. In HTTP, many status codes can give you an idea of what was going on when you called an API.
Streaming analytics is crucial to modern business – it opens up new product opportunities and creates massive operational efficiencies. In many cases, it’s the difference between creating an outstanding customer experience versus a poor one – or losing the customer altogether. However, in the typical enterprise, only a small team has the core skills needed to gain access and create value from streams of data.
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 For many, despite the best intentions, their metrics are too simplistic, narrow, and based on outdated models of engagement.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.