Systems | Development | Analytics | API | Testing

The very system designed to keep you comfortable is the same one keeping you trapped. You pay only for rows that change. Simple, right? But the reality is quite different. MAR mechanics are designed to blow up when you scale. You expect to be paying for real data changes but you end up paying for the whole row, regardless of how little data you move on it. It lures you in with simplicity but the underlying mechanics is an alternate reality. Every connection follows its own pricing curve, the costs stop behaving logically, and forecasting your bill turns into a nightmare.

What does it really take to build AI-ready data systems people can trust? In this episode of Data Builders Club, James Serra shares lessons from 40+ years in data and AI, covering data quality, trust, Data Mesh trade-offs, real-time systems, and why strong foundations matter more than hype in the AI era. Featuring insights from James Serra, Data & AI Solution Architect at Microsoft and author of Deciphering Data Architectures.

Across 8 years and 2,000+ data teams in 40+ countries, three principles have shaped every decision we've made. That's the conviction behind Hevo's next chapter. In our latest video, Manish Jethani, Founder & CEO at Hevo Data, along with Scott Husband, Director of Partnerships, and Amit Gupta, VP of Engineering, walk through what's changed under the hood, and why every architectural decision traces back to three non-negotiables: Reliability, Simplicity, and Transparency.

AI is no longer an external layer. With Snowflake AI capabilities, combined with a real-time ingestion layer from Hevo, you can now build internal and external data products and AI workflows directly within Snowflake.

Everyone is investing in AI, but most teams are blocked by one thing: their data isn’t ready. Data is scattered across SaaS tools, pipelines break silently, and insights are delayed. Without fresh, reliable, and centralized data, AI models, dashboards, and real-time use cases simply don’t work.

Your data team doesn’t need more tools. It needs fewer bottlenecks. What if you could go from raw data to production-ready pipelines and AI workflows in a single day? With Snowflake’s Cortex Code, teams can now build, optimize, and deploy data workflows using natural language, dramatically accelerating development inside the warehouse.

Introducing Episode 1 of Data Builder Club: a series to celebrate the data leaders behind the most impactful data systems. In this episode we sit down with Matt Forrest, Director of Customer Engineering at Wherobots, geospatial advocate, and LinkedIn's go-to voice for modern data and spatial engineering. Matt opens up about his unconventional path into data, his philosophy around building reliable geospatial systems, and why a good foundation is the only thing that makes everything else possible.

Every company has an AI roadmap. Very few have the data infrastructure to execute it. At Hevo Data, we've spent 8 years building pipelines that are reliable, simple, and transparent so 2,000+ data teams can build without second-guessing their data. We sat down with Manish Jethani, Amit Gupta, and Scott Husband to talk about what comes next. If your data isn't AI-ready, your roadmap stays a roadmap. We've re-engineered the platform to serve as the context engine your AI vision actually runs on. Because the models are only as good as the data underneath them.

applications, and AI systems. But orchestration alone does not solve one of the biggest operational challenges: reliable data ingestion. In this live session, we explore how integrating Hevo directly into Airflow workflows creates a reliable foundation for modern ELT pipelines. Through native operators, sensors, and triggers, teams can orchestrate ingestion, monitor pipeline health, and ensure downstream analytics and AI workloads always run on trusted data.

Modern data pipelines don’t fail loudly. A schema change slips through. A few bad records halt ingestion. Dashboards go stale. Engineers rerun backfills. Warehouse costs spike. Business teams begin to question the data. Pipeline instability and silent failures remain some of the biggest bottlenecks for analytics teams operating at scale.