Contract-first API development involves defining the API contract before any coding begins. This method ensures all teams are aligned on the API’s structure and functionality from the start. In this article, we’ll delve into what contract-first API development is, its benefits, and how to implement it effectively.

According to Gartner’s 2023 report, 60% of the organizations surveyed automate their software testing to boost product quality, 41% reported using it to speed up deployment, and 29% say it helps alleviate their QA teams’ workload. Also, 43% of these organizations claimed that test automation results in higher test accuracy. Despite automation’s large-scale adoption and various benefits, many QA leaders and test engineers find its implementation challenging.

In the world of APIs, there are many different architectural styles for building APIs, and each one has its own benefits, cons, and ideal use cases, but two prominent approaches dominate the landscape: gRPC and REST. Both have their unique strengths and weaknesses, making them suitable for different scenarios. In this blog, we’ll delve into the key differences between gRPC and REST, explore their use cases, and help you decide which one might be the best fit for your project.

With today's fast-paced business environment, efficiency in data exchange holds the key to competitiveness and overall effectiveness in operations. EDI (Electronic Data Interchange) contributes massively to achieving this efficiency, redefining the way companies handle their data transactions. EDI is vital for doing business today. These automated workflows enable reduced transaction costs and improved data accuracy.

Have you ever faced a stubborn ‘403 Forbidden’ or celebrated a ‘200 OK’? HTTP status codes, specifically the “HTTP status code,” are the universal language of web servers, telling you the fate of your online requests. This guide takes you through each HTTP status code, offering insights to resolve and leverage these responses for better web interaction efficiently.

How to use BigQuery vector search to analyze logs and asset metadata stored in BigQuery tables.

In the age of AI, enterprises are increasingly looking to extract value from their data at scale but often find it difficult to establish a scalable data engineering foundation that can process the large amounts of data required to build or improve models. Designed for processing large data sets, Spark has been a popular solution, yet it is one that can be challenging to manage, especially for users who are new to big data processing or distributed systems.