The Director of Product Management at AWS sat down with Fivetran to unpack how Iceberg and metadata are reshaping the future of data lakes, data warehouses and AI.

For the last couple of years, Retrieval-Augmented Generation (RAG) architectures have become a rising trend for AI-based applications. Generally speaking, RAG offers a solution to some of the limitations in traditional generative AI models, such as accuracy and hallucinations, allowing companies to create more contextually relevant AI applications.

What do you think of when you think of generative AI? Generating photos, animations, and videos? Coding and solving math problems? Writing content and brainstorming with a chatbot? These have all driven plenty of excitement around AI, but there’s so much more to it than that! From an enterprise perspective, Generative AI’s impact on intelligent document processing technology is remarkable.

Producing high-quality content on a consistent basis is no small feat. Even seasoned content teams struggle to handle the pressure of churning out work that resonates, converts and ranks (especially with Google’s slew of algorithm updates ). At companies with smaller in-house marketing teams without specialized content professionals? That pressure can feel…crushing.

Some of the popular AI tools people and corporations are using now include ChatGPT, Google Gemini, and Microsoft Copilot. This has resulted in higher usage and adoption of this technology and this has caused some worry among people, particularly in terms of employment. However, for software testers, these changes should be seen as a chance to improve rather than a threat.

While still in its early days, artificial intelligence is becoming a driving force behind innovation in software testing. While automation has improved testing efficiency, AI can take it further by influencing critical decision-making. Rather than reacting to issues as they arise, teams can now identify potential problems earlier in the development cycle. In this article, we’ll explore how artificial intelligence can help teams rethink their testing strategies.

Welcome back to the third installment of our blog series where we’re diving into the beautiful black box that is Apache Kafka to better understand how we interact with the cluster through producer and consumer clients. Earlier in the series, we took a look at the Kafka producer to see how the client works before following a produce request as it’s processed by the cluster.