Systems | Development | Analytics | API | Testing

Quickly create Data APIs

Creating a Data API for your organization is crucial to digital transformation. In this video, we will be going into detail on how to set up a data API and how it can easily be hosted and tested using Linx. You can create an API and define endpoints using a wizard. Read from any data source, and host your API in under 10 minutes, depending on complexity and scale. You can also test your API locally before deploying, meaning you can view precisely what your clients will see, even before your application hits your dev server.

3 Ways to Break Down SaaS Data Silos

Access to data is critical for SaaS companies to understand the state of their applications, and how that state affects customer experience. However, most companies use multiple applications, all of which generate their own independent data. This leads to data silos, or a group of raw data that is accessible to one stakeholder or department and not another.

API-Managed Event Streaming with Kong Konnect and Amazon MSK

Event streaming allows companies to build more scalable and loosely coupled real-time applications supporting massive concurrency demands and simplifying the construction of services. Ultimately, we may need to grant access to such infrastructure to the most diverse entities, including external applications posting events that will be eventually consumed by internal systems. The purpose of such exposure is to promote development teams’ collaboration.

Kong Insomnia 2023.2.0 Released with Enhanced Import Flow

We’re thrilled to announce the latest release of Kong Insomnia, version 2023.2.0, packed with improvements and fixes that will make it even easier to manage your APIs. In this release, we introduce an enhanced Import flow, along with support for Kong 3.0 and the return of Swagger/OpenAPI preview. Continue reading to learn more about these exciting updates.

The Pros and Cons of Data Mesh vs Data Lake

Data has become the lifeblood of modern businesses, and organizations are constantly looking for ways to extract more value from it. While there isn’t a one-size-fits-all solution for data management, organizations tend to take some common approaches. Two popular approaches to managing data are Data Mesh and Data Lake. Data meshes and data lakes have recently become popular strategies for groups that want to avoid silos so they can make data-driven decisions.