According to Statista, the microservices architecture market will grow at a robust CAGR of 19.6%, reaching $10.86 billion by 2027, up from $4.44 billion in 2022. The shift to microservices architecture has revolutionized software design and development, significantly enhancing agility, scalability, resilience, delivery times, cost-effectiveness, and more. Since Dr.

Enterprises are beginning to implement gen AI across use cases, realizing its enormous potential to deliver value. Since we are all charting new technological waters, being mindful of recommended strategies, pitfalls to avoid and lessons learned can assist with the process and help drive business impact and productivity. In this blog post, we provide a number of frameworks that can help enterprises effectively implement and scale gen AI while avoiding risk.

As automation capabilities grow, so do the related use cases. It’s not surprising that 80% of executives think automation can be applied to any business decision, according to a Gartner survey. But as implementation becomes easier and automation adoption more prevalent, your organization can quickly lose control, with too many automation projects happening in silos.

Struggling with API issues? API observability may have the solution. It reveals the critical insights that hide within API metrics, logs, and traces, offering you the power to diagnose and fix problems swiftly. In this article, we detail what API observability is, how it benefits your systems, and the ways it supersedes traditional monitoring to keep your digital services operating at peak performance.

HTTP status codes are an essential part of web communication. They provide information about the outcome of a request made to a server. Whether you’re a seasoned developer or a student just starting out, understanding these codes can significantly enhance your ability to debug and optimize web applications. This blog will break down the most common HTTP status codes that one generally encounters, explain their meanings and how to handle them.

Nowadays, businesses need to integrate with various applications and services to streamline operations and improve customer experience. Application Programming Interfaces (APIs) play an important role in enabling this integration by letting different software systems to communicate and share data. API integration platforms are the key to maximizing the potential of your APIs.

When creating an ODBC driver, it is essential to convert data from the data source into a relational format. This is necessary because ODBC and SQL require and operate on data that is organized with a fixed schema and returned in a tabular structure.

When two different software applications want to join forces by sharing information, it’s not always a perfect union. Different database structures, programming languages, and network protocols tend to complicate the process. When reading about the various API technologies, you’ve probably come across two terms, OData vs REST, and wondered, “what’s the difference?” It turns out that OData vs. REST are two closely related concepts.

When Snowflake acquired the TruEra AI Observability platform, we committed to keeping TruLens open source. We’re not only keeping that promise; we’re emphasizing it. Our goal remains to support LLM app developers in creating trustworthy generative AI applications. In the weeks since the acquisition, we have already added ecosystem-friendly enhancements including: We plan to continue making enhancements and improvements that benefit the community at large, whether on Snowflake or not.

If you’re one of the cool kids building AI-based products you’ve probably heard of — or are already doing — RAG. If you’re not, let me tell you what RAG is before telling you one weird fact about it. “Retrieval-Augmented Generation” is nothing more than a fancy way of saying “including helpful information in your LLM prompt.” Still, there are many ways to do it and many questions to answer when building a RAG pipeline.