In the rapidly evolving landscape of API management, understanding the raw performance and reliability of your API gateway is not just an expectation — it's a necessity. At Kong, we're dedicated to ensuring our users have access to concrete, actionable data to guide their operational and development strategies.

According to the U.S. Energy Information Administration (EIA), world energy consumption is forecasted to surge by almost 50% by 2050. This prediction underscores the urgent need for a concerted global push towards smarter, more efficient energy grids and resource management. According to the International Energy Agency (IEA), the global Internet of Things (IoT) deployment in power systems is expected to save $80 billion annually from 2016 to 2040, making up 5% of the total global power generation costs.

In this post, we're going to explore Karpenter, the ultimate solution for Node Autoscaling. Karpenter provides a cost-effective capability to implement your Kong Konnect Data Plane layer using the best EC2 Instances Types options available for your Kubernetes Nodes. See the previous posts in this series for more on Data Plane Elasticity and Pod Autoscaling with VPA, HPA, and Node Autoscaling with Cluster Autoscaler on Amazon EKS 1.29.

Application programming interface (API) automation testing is a type of automated testing that focuses on the performance and functionality of APIs. This process can test APIs for correctness, compatibility, and efficiency. API automation testing can ensure that APIs function properly and meet consumers’ expectations. Here’s the key things to know about API automation testing: Table of Contents.

With the power of Fivetran and Databricks Serverless, you have a front-to-back solution for all of your data workloads.

Here at SmartBear, our motto is that quality isn’t just the goal, it’s the whole point. The software development process is intricate and dynamic, with constant changes being made to meet evolving user requirements and address defects. However, with these changes come potential risks. One of the most significant challenges in software development is ensuring that making modifications to an application does not inadvertently break existing functionality.

With Node.js and Express, you can easily create a JavaScript server. Then the obvious question would be, “Why Typescript?” While JavaScript remains the most popular scripting language, TypeScript emerges as a great alternative when your application becomes more complex or when you collaborate with a distributed team of developers.

Unlock enhanced platform engineering with traffic replay for real-world performance testing, ensuring faster feature delivery with consistent quality.

Artificial intelligence (AI) apps are becoming increasingly crucial for individual customers and businesses alike. These apps bring many benefits, such as task automation, efficient analysis of large data sets, and data-informed decision-making, making AI-powered applications highly valuable. As a result, DevOps teams working on AI apps can’t afford poor performance.

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.