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.

Every enterprise seeks to gain competitive advantage through the embrace of new, innovative technologies to either transform external customer engagement or unlock internal operational efficiencies. Test automation represents a powerful technology innovation by which product teams can unlock significant software development operational efficiencies. But test automation transformation initiatives come with notable risks that should be considered.

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According to the NIST research, the cost of detecting and fixing software defects exponentially increases over time. Shift left testing highlights the importance of testing early and frequently in the software development lifecycle (SDLC) to ensure that errors are identified and fixed as soon as possible. This method aligns closely with Agile Testing and DevOps Testing philosophies, which emphasize early and continuous integration.

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.