Today we’re excited to announce that Kong Insomnia has shipped Storage Control, a new enterprise capability that allows administrators in an Insomnia organization to mandate what type of backend storage developers are allowed to use when creating API assets like API collections, API specifications, environments configuration, authentications data, and more.

Looking to develop more APIs faster (and securely)? Then you have some different API platform deployment models to consider. In this post, we'll define and compare the siloed, centralized, and federated API platform deployment models — and dive into why federated API management is key to striking a balance between agility and governance.

When it comes to cloud data warehousing, the choice between Snowflake vs BigQuery is crucial for businesses that rely on big data analytics for decision-making. Snowflake, known for its flexibility and ease of use, contrasts with BigQuery, Google’s fully-managed, serverless data warehouse that excels in speed and scalability. Understanding the main differences between these two platforms is essential for selecting the right solution that aligns with your data strategy and operational needs.

In this article, we’re diving deep into the nuts and bolts of creating a private API gateway. From the initial installation to securing your API with keys and roles, we’ll walk you through every step of the process. Whether you’re looking to connect to a Microsoft SQL Server database or any other popular database, DreamFactory makes it a breeze, automating the tedious parts and letting you focus on what matters most: your data.

Connecting multiple systems and exchanging data among them is afrequent requirement in many business scenarios. This typically involves one or many source systems, an intermediary processor, and one or many destination systems. Some organizations invest in purpose-built solution suites such as Data Warehouse, Master Data Management (MDM), or Extract, Transform, Load (ETL) platforms, which, in-theory, cover a wider spectrum of requirements.

Did you know that the average enterprise utilizes over 1,500 APIs to drive its digital operations? APIs play a pivotal role in today’s data-driven landscape. However, few know the impact API architecture has in enhancing API performance.

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.

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.

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.

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.