Systems | Development | Analytics | API | Testing

Over the past year, interest in artificial intelligence has surged due to the proliferation of generative AI and large language models. These tools captured imaginations, demonstrating a technology brimming with possibility. While many focused on the potential of these tools, some companies made AI practical. For example, last year, Appian released packaged AI tools for processing content at scale and quickly building interface forms.

In the realm of software development, choosing to stay with one vendor is the best choice for certain specific projects. However, sometimes you might need to switch for financial reasons, the need for advanced technical skills, or to update your technology stack. When this happens, managing a smooth transition is key to maintaining continuity and quality. So, what’s the best way to handle it?

Because human-machine interaction using natural language is now possible with large language models (LLMs), more data teams and developers can bring AI to their daily workflows.

Snowflake has created an ecosystem where data is not just an asset but the backbone of innovation and operational efficiency. With regard to Snowflake, DreamFactory Software offers a robust platform for developing internal or private APIs that serve as crucial conduits for these data products. Our integration with Snowflake through dedicated connectors is transforming the way businesses access, analyze, and utilize their data.

Artificial Intelligence (AI) is primed to reshape the way just about every business operates. Cloudera research projected that more than one third (36%) of organizations in the U.S. are in the early stages of exploring the potential for AI implementation. But even with its rise, AI is still a struggle for some enterprises. AI, and any analytics for that matter, are only as good as the data upon which they are based. And that’s where the rub is.

Managing complex data pipelines is a major challenge for data-driven organizations looking to accelerate analytics initiatives. While AI-powered, self-service BI platforms like ThoughtSpot can fully operationalize insights at scale by delivering visual data exploration and discovery, it still requires robust underlying data management. Now, that’s changing. Snowflake's new dynamic tables feature redefines how BI and analytics teams approach data transformation pipelines.

Ensuring proper data governance and security relies on the capabilities offered by the software and platforms constituting your data infrastructure.

In an era where data is a critical business asset, securing it in B2B integration scenarios is more important than ever. This article explores the essential strategies and technologies for robust data security in B2B contexts. Data is a critical asset for businesses, which makes securing it during B2B integration more important than ever. This article will explore the best practices and tools that can aid in robust data security. Here are the 5 key takeaways from our B2B integration article.