Systems | Development | Analytics | API | Testing

Generics in Go refers to a language feature that allows creating functions, data structures, and interfaces that can work with different types. In other words, generics enable the creation of code that is not limited to a specific type or data structure. Before the introduction of generics in Go, developers had to write multiple functions to handle different types of data. This approach was often cumbersome and led to code duplication.

Have you ever wondered what happens when you visit a website or use an app that takes a long time to load? It seems to take an eternity at times! This is where software testing comes in. Use our load test tool you can test how well a website or app can handle a large number of people, all while using it at the same time.

The future of data integration is no-code data integration platforms, and we have the research to prove it.

There are many factors to keep in mind when thinking about how to keep systems and applications secure. First and foremost is the actual environment where the application is deployed. The environment consists of the Operating System (OS), libraries included with the OS, and software that runs in the background. In a perfect world, an application would have very few or zero dependencies on the environment, thereby eliminating the need to keep dependencies up to date.

Lost in the talk about OpenAI is the tremendous amount of compute needed to train and fine-tune LLMs, like GPT, and Generative AI, like ChatGPT. Each iteration requires more compute and the limitation imposed by Moore’s Law quickly moves that task from single compute instances to distributed compute. To accomplish this, OpenAI has employed Ray to power the distributed compute platform to train each release of the GPT models.

In today's data-driven world, product analytics is crucial in understanding user behavior, improving product features, and driving business growth. However, product analytics alone may not provide a complete picture of user interactions and business performance. Integrating product analytics with other data sources and systems is essential to gain deeper insights and make more informed decisions.

When it comes to choosing a business intelligence (BI) solution vendor for your business, there are a variety of factors to consider. One important area of comparison is the natural language query (NLQ) features offered by different BI vendors. NLQ is increasingly becoming a key capability of the modern self-service analytics experience, as it allows users to ask complex questions of their data, and receive insightful answers in the form of a pre-generated, best practice data visualizations.

Chances are high that you've previously worked with Express, as it's been the go-to web framework for Node.js developers since its release in 2010. However, in recent years, newer web frameworks have emerged, and Express's development has slowed down significantly. Fastify is a relatively new player on the scene, but it's quickly gaining popularity due to its speed and unique features. If you're still using Express, you might wonder if it's worth switching to Fastify.

Practical advice for starting your journey to analytics excellence.

In my previous post on scaling Kong deployments with and without a database, we covered the concepts of deploying Kong with and without a database, as well as using decK, distributed, and hybrid deployments. In this article, we take a tour of some of the possible Kubernetes deployments of Kong. Kubernetes (K8s) is the container orchestration war winner. While there are still deployments using other engines, we see K8s far more.