Systems | Development | Analytics | API | Testing

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Time-Tested Insights on Creating Competitive API Programs

When the Application Program Interface (API) first came into existence, developers viewed it as a revolutionary approach to creating re-usable software fragments. Instead of creating new code from scratch for every new program, they could now use existing functionality to develop new features. Not only did this decrease the amount of time needed to deploy a program but also meant they could leverage existing code which was already tried and tested.

3 Key Pillars of Building a Culture of Quality in Engineering

Building a high-quality product takes teamwork. Maintaining a best-in-class product while continually developing high-quality features and giving a stellar customer experience, takes a quality-driven culture. We teamed up with Invotra in this new guide, Building a Culture of Quality: How Teams Can Use Quality to Achieve Business Goals, to explore the strategy behind building a culture of quality.

Support Virtual Services at Scale with VirtServer

Virtual services are changing the way teams are developing and testing applications across various industries. While SmartBear's ServiceV solution makes it simple to build and run these virtual services locally, deploying them to be used by other teams opens up the power of virtualization to an entire organization.

Can You Trust Your Analytics Dashboard? 3 Steps To Build a Foundation of Trusted Data

We are in the era of the information economy. Now, more than ever, companies have the capabilities to optimize their process through the use of data and analytics. While there are endless possibilities to data analysis, there are still challenges with maintaining, integrating, cleaning it to ensure that it will empower the people to take decisions.

6 Strategy Elements for Building Cloud Native Applications

The cloud native paradigm for application development has come to consist of microservices architecture, containerized services, orchestration, and distributed management. Many companies are already on this journey, with varying degrees of success. To be successful in developing cloud native applications, it’s important to craft and implement the right strategy. Let’s examine a number of important elements that must be part of a viable cloud native development strategy.

Part 3: How machine learning, AI and automation could break the BI adoption barrier

In the first blog post of the series, we saw the dire state of analytics adoption. This problem feeds into the low usage and governance of data across organizations. Then, in the second post, we saw how the evolution of analytics has brought us to a prime position for augmented analytics. But will this new wave of augmented analytics break through the barriers to BI adoption?