Systems | Development | Analytics | API | Testing

Governing GraphQL APIs with Kong Gateway

Modern software design relies heavily on distributed systems architecture, requiring all APIs to be robust and secure. GraphQL is no exception and is commonly served over HTTP, subjecting it to the same management concerns as any REST-based API. In fact, GraphQL’s dynamic client querying capabilities may lead to more complex and potent attack surfaces than traditional REST-based APIs.

Bank Risk Management: Top Challenges and Their Solutions

In today's dynamic financial landscape, threats are not only escalating but also rapidly evolving. At the same time, regulators impose stricter transparency and compliance requirements on financial institutions. So, how can decision-makers navigate this challenging environment, with relentless cyber threats coming at them from one side and rising regulatory demands from the other?

ETL and Data Warehousing Explained: ETL Tool Basics

Understanding ETL (extract, transform, and load) and data warehousing is essential for data engineering and analysis. As businesses generate large amounts of data from different sources, efficient data integration and storage solutions become crucial. This article breaks down ETL and data warehousing, providing insights into the tools, techniques, and best practices that drive modern data engineering.

See How Much Your APM is Costing You to Monitor Node.js Apps

We are excited to share the release of our new Cost Calculator to showcase just how much the wrong APM provider can add to your cloud hosting costs (try it now). Observability is vital, but it comes with computational overhead that shares the same infrastructure as your application. This is compounded in typical Node.js APM tooling due to the internal workings of Node.js itself.

Four Ways Telcos Can Realize Data-Driven Transformation

Telecommunications companies are currently executing on ambitious digital transformation, network transformation, and AI-driven automation efforts. While navigating so many simultaneous data-dependent transformations, they must balance the need to level up their data management practices—accelerating the rate at which they ingest, manage, prepare, and analyze data—with that of governing this data.