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.

As I reflect (although a better word would maybe recover) on the incredible experience at Qlik Connect in sunny Orlando, I am filled with excitement and pride. This year’s event was particularly special because it was our first opportunity to showcase the expanded Qlik portfolio, including our robust data foundation, Qlik Talend Cloud, and Qlik Answers, our all-new knowledge assistant that delivers personalized, relevant answers to questions generated from a variety of unstructured data sources.

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.

In this post, we demonstrate how a robust and scalable generative artificial intelligence (GenAI) chatbot is built using Amazon Bedrock and Confluent Cloud. We walk through the architecture and implementation of this generative AI chatbot, and see how it uses Confluent's real-time event streaming capabilities along with Amazon's infrastructure to continually stay up to date with the latest advances from the AI landscape.

In the fast-evolving landscape of data science and machine learning, efficiency is not just desirable—it’s essential. Imagine a world where every data practitioner, from seasoned data scientists to budding developers, has an intelligent assistant at their fingertips. This assistant doesn’t just automate mundane tasks but understands the intricacies of your workflows, anticipates your needs, and dramatically enhances your productivity at every turn.

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.

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.

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.