By Lior Gavish
AI coding agents are writing SQL, building pipelines, and modifying schemas at a pace that would have been unimaginable two years ago. They’re fast, tireless, and increasingly capable. But they lack something every experienced data engineer has: the operational instincts to do data work responsibly. They don’t feel accountable like humans do, and they certainly … Continued
By Lior Gavish
You might not consider scalability a critical limitation of enterprise AI—but development velocity is undoubtedly at the top of executive agendas this year. Nearly every SaaS tool today comes with some sort of AI feature to expedite something or other—and that’s mostly a good thing. AI is unlocking new capabilities for teams of all sizes, … Continued
By Lior Gavish
Turn data health insights into actionable frameworks in half the time with an AI agent built specifically for data + AI teams
By Lior Gavish
Data quality monitoring is one of the traditional methods of managing data quality—but is it enough? Find out what’s missing, and how to do it better.
By Lior Gavish
Generative AI is taking the world by storm – here’s what it means for data engineering and why data observability is critical for this groundbreaking technology to succeed.
By Lior Gavish
Monte Carlo and Databricks double-down on their partnership, helping organizations build trusted AI applications by expanding visibility into the data pipelines that fuel the Databricks Data Intelligence Platform.
By Lior Gavish
With their extended partnership, data + AI observability leader and the Data AI Cloud bring reliability to structured and unstructured data pipelines in Snowflake Cortex AI. – Announced today, Monte Carlo and Snowflake are delivering end-to-end observability across both structured and unstructured data pipelines powering agentic AI applications in Cortex AI, the AI Data Cloud’s … Continued
By Lior Gavish
A beginner’s guide to implementing the latest industry trend: a data mesh.
By Lior Gavish
Leverage machine learning to proactively identify data and data pipeline anomalies so you can fix bad data before it impacts your analytics, AI and other data products.
By Lior Gavish
Organizations are racing to deploy generative AI applications to unlock new sources of value and stave off potential disruptors as this transformative technology takes hold. LLMs have quickly become plug-and-play APIs while smaller specialized models are becoming rapidly commoditized as well. To differentiate and expand the usefulness of these models, organizations must augment them with … Continued
