APIs, or application programming interfaces, allow different software systems to communicate. But different types of APIs exist. Each type serves a different purpose. And each type has its own pros, cons, and use cases. This article breaks down the main API categories—open, partner, internal, and composite. We also discuss the types of APIs based on the protocols they use. At the end of this article, you can confidently choose the right one for your needs.

Digitalization has led to more data collection, integral to many industries from healthcare diagnoses to financial transactions. For instance, hospitals use data governance practices to break siloed data and decrease the risk of misdiagnosis or treatment delays. Therefore, managing data to protect its integrity and security leads to high-quality, reliable data that empowers organizations to make informed decisions.

Organizations increasingly use data to gain a competitive edge. Two key disciplines have emerged at the forefront of this approach: data science and data analytics. While both fields help you extract insights from data, data analytics focuses more on analyzing historical data to guide decisions in the present. In contrast, data science enables you to create data-driven algorithms to forecast future outcomes. These disciplines differ significantly in their methodologies, tools, and outcomes.

Built on the foundation of Apache Spark, Databricks is a unified, open data lakehouse platform that empowers customers to efficiently and cost-effectively process, store, manage, and analyze large volumes of enterprise data.

Cypress only supports JavaScript and Selenium offers more flexibility, but each has their benefits and drawbacks. Find out which testing option is right for you.

While AI has garnered the attention of many industries, nanotechnology still has not lost its significance in healthcare and life sciences. Promising research continues to drive the exploration of nanotechnology for health and wellness applications. Yet, an often-overlooked element is the crucial role of software testing in this field.

Learn how a data fabric can add value by maximizing the value of a company’s data infrastructure.

Data analytics is the science of analyzing raw data to draw conclusions about it. The process involves examining extensive data sets to uncover hidden patterns, correlations, and other insights. With today’s technology, data analytics can go beyond traditional analysis, incorporating artificial intelligence (AI) and machine learning (ML) algorithms that help process information faster than manual methods.