Systems | Development | Analytics | API | Testing

It's a fact that the presence of smartphones in our lives has evolved into a globally dominating reality today. According to a recent reports by Statista, 85.74% of the general population, or roughly 6.93 billion people, are using smartphones and over 7.33 billion people are using mobile phones. This huge figure points to the fact that smartphones are being adopted fast and have become essential to our day-to-day lives.

Curious about Gatling load testing for Kubernetes? Learn how it compares to Speedscale when it comes to functionality, setup, CI/CD integration & more.

In this post, we'll turn to the last piece of the puzzle in LiteStack: Litesearch. As an example, we will equip a prompts index page with a search bar to query a database for certain prompts. We will generate a couple of fake records to test our search functionality against. Let's get to it!

In a world full of microservices and distributed systems, building robust and reliable APIs is of paramount concern. One key concept that plays a vital role in achieving reliability is idempotency. In this article, we'll delve into the world of idempotency, exploring what it is, why it's important, and how it can be implemented to solve the persistent problem of duplicate processing in APIs. Along the way, we'll see how to build an idempotent API in Node.js with Redis.

In the bustling world of web development, where time is as precious as a cold beer on a hot day, automation has become not just a luxury but a downright necessity. Enter Playwright, the shining knight in digital armor, developed by the tech wizards over at Microsoft.

With the increasing importance of real-time data in modern businesses, companies are leveraging distributed streaming platforms to process and analyze data streams in real time. Many companies are also transitioning to the cloud, which is often a gradual process that takes several years and involves incremental stages. During this transition, many companies adopt hybrid cloud architectures, either temporarily or permanently.

Apache Iceberg has recently grown in popularity because it adds data warehouse-like capabilities to your data lake making it easier to analyze all your data—structured and unstructured. It offers several benefits such as schema evolution, hidden partitioning, time travel, and more that improve the productivity of data engineers and data analysts. However, you need to regularly maintain Iceberg tables to keep them in a healthy state so that read queries can perform faster.