First in a 3-part series on self-service K2K replication. This post tackles how to give self-service access to deploy K2K without handing over the keys to your Kafka clusters. Lenses developed K2K (Kafka-to-Kafka) to solve two major problems: This includes making it as self-service as possible so developers can deploy without requiring a PhD in MirrorMaker2. One key design requirement: don’t force engineers to manage credentials to authenticate with Kafka.

Learn what data orchestration is and how it differs from ETL. Explore example data orchestration workflows and top tools to automate your data workflows.

We've officially launched the latest version of Ask Yellowfin, our helpful AI-powered chatbot designed to help you get the most out of the platform without having to dig through documentation or wait on support.

Right now, many SaaS leaders are wondering how AI will change building and scaling software companies? AI is transforming how we build software, how teams operate, and how quickly companies launch new products. According to Adam Robinson, founder and CEO of Retention.com, there’s something that most leaders overlook. Your problems won’t get solved by AI but by product-market fit.

AI can now generate SQL, build dashboards, and answer questions in plain language. But generating queries isn’t the same as understanding a business. The model might not know which revenue definition finance approves, how your fiscal calendar works, or which fields require restricted access. As AI agents become the front door to analytics, the real challenge isn’t query generation; it’s semantic grounding. That’s where the Agentic Semantic Layer becomes essential.

Most executives are drowning in dashboards but starving for insights. We’ve been conditioned to view “analytics” as a rear-view mirror, a report on what happened, rather than a steering wheel for what should happen next. Traditional BI creates a “reporting tax,” where scaling insights requires a proportional increase in data analyst headcount to interpret the noise.