Maintaining standards in an ever-growing codebase that multiple developers contribute to can be difficult and tedious. Ensuring that the codebase follows best practices and does not deviate from the standards is essential for any project. But this is typically something that can only be enforced manually by code reviews and other similar processes. As with any other manual task, this can be time-consuming, tedious, and error-prone. That's where architecture testing comes in.

By combining speech-to-text with BigQuery's analytics capabilities, you can understand things like customer sentiment and recurring product issues.

An API (Application Programming Interface) is a set of rules and protocols that let software programs communicate with each other. It acts as an intermediary between different software or services. For example, when using a mobile app to book a hotel room, the API allows the app to communicate with the hotel’s booking system. While many REST API developers have their way of doing things, some factors in API development are generally considered essential.

Application programming interfaces (APIs) have quickly become vital to modern businesses. APIs work by allowing different systems to talk to each other and share data. The commercial use of APIs and web APIs has skyrocketed over the last decades in accordance with the rise in app development, mobile apps, and e-commerce. However, how do you estimate API development time? Well, API creation and the development time frame will always vary from project to project.

Outsourced engineering teams are gaining in popularity and many investors have started to treat them as a requirement.

An effective data governance strategy is crucial to manage and oversee data effectively, especially as data becomes more critical and technologies evolve. This strategic approach to data governance aligns with findings from a McKinsey survey, suggesting that companies with solid data governance strategies are twice as likely to prioritize important data — leading to better decision-making and organizational success.

As the cybersecurity threat landscape continues to evolve globally, organizations operating in the financial sector are seeing regulations shift to address the associated risks, and none may prove more impactful than the European Union’s (EU) Digital Operational Resilience Act (DORA). This regulation aims to strengthen the operational resilience of financial entities (FEs), and their third-party information and communication technology (ICT) providers.

There is an increasing demand for openness and accountability in Artificial Intelligence (AI) decision-making processes. Explainable Artificial Intelligence (XAI) is a developing idea as organizations attempt to comprehend and trust AI’s suggestions and insights. Gartner expects that explainable AI will reach the pinnacle of expectations. Over the next fifteen years, XAI will enter the decisive stages of development.

In the field of cybersecurity, it's important to have a thorough awareness of potential attack surfaces and vulnerabilities in order to stay one step ahead of bad actors. A survey from the Ponemon Institute states that, on average, data breaches cost an astounding $4.45 million worldwide in 2023.