Smoke testing and regression testing play crucial roles in ensuring the functionality and reliability of software applications. Although both test software’s functionality, their purposes differ, and they occur at different stages of development. Understanding the differences between these two testing types can help effectively manage and maintain software quality.

While Salesforce is a much celebrated CRM tool in the industry, it’s no perfect tool just like any others. It offers limited flexibility for on-the-fly data manipulation compared to Excel. Salesforce can be complex to set up and use effectively, particularly for users without technical expertise. It often requires training or support from Salesforce administrators to fully utilize its capabilities.

A quality assurance (QA) test plan or a software test plan is a document that outlines the steps, approaches, tools, and best practices for carrying out QA testing for your project. The purpose of a QA test plan is to define testing goals and objectives while considering cost, project requirements, and delivery timelines. Ultimately, this document ensures that all stakeholders are unified in their commitment to delivering a high-quality product.

Ramkumar Nottath, the Senior Solutions Architect at AWS, beautifully puts it. And that’s where low-code or no-code ETL platforms can help—to make the data consumable and democratize it. In this blog, I explain low-code vs. no-code from my experience.

Organizations today are accumulating data more than ever. Traditional data management approaches, such as centralized data warehouses and siloed data marts, are struggling to keep pace with the ever-increasing volume, velocity, and variety of information. The complexity of modern data environments is outpacing the capabilities of these legacy systems and demands a more agile and distributed solution.

Workflows determine how organizations conduct processes. These workflows can initially be very straightforward—just a simple series of steps that must be completed sequentially to achieve a particular outcome. However, the steps can become more complicated as business processes and requirements become more extensive. Your organization needs to keep these workflows running smoothly even as they become more complicated.

In 24.2, we introduced our enterprise copilot. Enterprise copilot allows you to upload business documents and collect them in knowledge sets. From there, you can ask questions about information in these documents and receive answers quickly. For instance, an organization with a heavy regulatory burden could upload legislative and operational documents. Then, these employees could get insights from Appian AI Copilot to ensure they adhere to compliance requirements.

Imagine launching your software with confidence, knowing that every button click, data transfer, and user interaction works perfectly—no surprises, no last-minute bugs. This level of assurance doesn't happen by chance; it’s the result of accurate End-to-End (E2E) Testing. But what exactly does end-to-end testing mean? How does it ensure that your application not only works but grows under real-world conditions?

In the field of Natural Language Processing (NLP), two prominent terms frequently arise: Small Language Models (SLMs) and Large Language Models (LLMs). While both play significant roles across various industries, including healthcare and life sciences (HC-LS), their applications and advantages diverge depending on the context and requirements of the task.

Your organization is likely sitting atop vast quantities of diverse data. However, all that data must undergo certain processes before you can fully leverage it. Data discovery is among those processes. Let’s learn more about it.