Networks are unreliable. Third-party dependencies, like APIs, are unreliable. This is why we build resiliency into our applications and services. Most prep work is focused around HTTP requests, but an aspect of them that is often overlooked is timeouts.

When you rely on a third party API for your application's features, it is important that you can reliably expect them work. Knowing that their uptime will be consistent, or greater than your own, and knowing that their support will be available if you identify a problem, can go a long way in making your choice of APIs easier. In this article we'll look at the Service Level Agreement, or SLA, and how it protects both you and the provider in the event of an outage or problem.

Integrating with APIs can be challenging. Each with its own documentation format, SDK patterns, and authentication quirks. Once you get the integration up and running, the harder step is testing and debugging any problems. This part can be exploratory—before you finish building—or as part of problem resolution.

Cloud-native organizations embracing microservices are running into an unavoidable security question: how to handle microservice authorization controls? The central problem is this: unlike monolithic app structures, microservices architectures expose dozens more functionality through APIs, which can leave them vulnerable to attack.

Are you taking data from an API in the format the web services gives it to you? You should not dictate the structure of data inside your application based on how an API provider structures their data. Instead, you can take advantage of the power of Python's list manipulation techniques to sort, filter, and reorganize data in ways that best suit your needs.

Manipulating data is a core skill for any developer. In an API-driven environment, so much of the data you receive is formatted in a way that doesn't directly match the way that your application or UI needs it. Each web service and third-party API is different. This is where the ability to sort, normalize, filter, and manipulate the shape of data comes in. In this article, we'll explore some common ways to work with data in Javascript.

One of Bearer's super powers is anomaly detection. Anomalies are unexpected issues that happen when making an API call. These could be high error rates, unexpected response codes, latency spikes, and more. By monitoring APIs with anomaly detection, we can identify problems with an API or within your application. Anomaly detection makes debugging easier and can help you identify API performance issues that affect your end users.

Did you know that you can use Bearer with serverless functions? While serverless, or cloud functions, might not be your first choice for making API calls they can be a great way to proxy API requests or even act as a lightweight API gateway. They also offer a great way to bring some of the benefits of Bearer into the Jamstack. The set up process is similar to installing the Bearer Agent into a traditional app, but there are a few things to watch out for.