Webhooks vs API, oh well. When there is a talk about what method to use in order to seamlessly sync datasets between applications, platforms and databases (in other words, to perform application integration), APIs are often named as today’s best-practices method for handling this.
As more companies invest in a cloud native infrastructure, they’re choosing to prioritize their applications as microservices—architecting them into distinct servers. Each component is responsible for one (and only one) feature. For example, you might have Server A responsible for handling billing logic, Server B for handling user interaction and Server C for handling third-party user interactions.
One of the best features in ThoughtSpot Everywhere is the Developer Playground. The Playground lets frontend Developers visually configure elements and generate JavaScript code to add into your web app. It is an amazing tool for testing and iterating configuration options before adding final elements such as Search, Liveboards, and visualizations into your web app. But what about a backend Developer who might be building solutions that utilize the Platform’s APIs?
At Kong, we run performance testing in CI in every commit or pull request that has a potential performance impact, as well as on each release. Thanks to the performance testing framework and its integration with Github Actions, we can easily get basic metrics like RPS and latency. Also, flame graphs to pinpoint the significant part that draws down performance. With that workflow in place, we figured one of the most significant parts of Kong’s hotpath is Nginx variable accesses.
As part of the Kong Gateway 2.6 release, we shipped a brand new jq plugin for anyone with an enterprise license to use. It’s like we combined the request and response transformer plugins to form a single, more powerful plugin—supercharging the way we work with request and response bodies. If you’re not familiar with jq, it’s a JSON processing language that allows you to manipulate any JSON document and transform it however you need.
Snowpark has generated significant excitement and interest since it was announced. Snowpark is a developer framework that enables data engineers, data scientists, and data developers to code in their language of choice, and execute pipelines, machine learning (ML) workflows, and data applications faster and more securely. While many parts of Snowpark are in preview stages, External Functions entered General Availability earlier this year.
Building, running and scaling SaaS demo systems that run around the clock is a big engineering challenge. Through the power of traffic replay, we scaled our demos in a huge way. A few weeks ago we launched a new demo sandbox. This is actually a second generation version of our existing demo system that I built a few months ago (codename: decoy). Because the traffic viewer page shows the most recent data by default, you need to constantly be pumping new data in there. Any type of real-time SaaS system is going to have a similar requirement. So this needs to be planned.
Without users, products mean nothing. So why is it that so many organizations analyze API and user behavior metrics as an afterthought? My theory would be that they just don’t have the right tools in place to actually collect, analyze, and use these usage metrics. With so many products aiming to improve their user experience and customer satisfaction, tools like Moesif are exactly what is needed.
