Updated June 3, 2025
AtScale 2025 Product Announcements: A Smarter, Faster, More Open Semantic LayerThe 2025 Semantic Layer Summit was more than a virtual event—it was a product showcase for the future of semantic modeling. AtScale unveiled its most significant updates yet, delivering innovations that make the semantic layer smarter, more interoperable, and GenAI-ready.…
Updated May 28, 2025
The 2025 Semantic Layer Summit: Why the Future of Analytics Starts with SemanticsEvery year, the Semantic Layer Summit gets a little bigger, bolder, and much more urgent. And 2025 was no exception. As enterprises push the limits of AI, demand trustworthy analytics, and struggle with ballooning data complexity, the industry is waking…
Updated May 22, 2025
Building Enterprise-Grade GenAI Applications: Key Takeaways from Our Expert PanelAs organizations accelerate their adoption of generative AI (GenAI), one thing has become clear: enterprise-grade AI isn’t just about plugging in a foundation model and hoping for magic. It’s about building intelligent systems that are secure, scalable, explainable, and tightly…
Updated May 13, 2025
How AtScale and Databricks Genie Democratize Data Intelligence with AI + a Semantic LayerIn our recent webinar, Democratizing Data Intelligence Across the Supply Chain with a Semantic Layer, I had the pleasure of speaking with Joshua Green, Data and AI Strategist at Databricks, about how our two platforms—AtScale’s Semantic Layer and Databricks Genie—are…
Updated May 1, 2025
Semantic Agents: Bridging the Gap between Data Silos and Actionable InsightsThis post was written by Dr. Arun Marar, PhD, MS, Dr. Prashanth Southekal, PhD, MBA, and Tobias Zwingmann, MS. It was originally published on the website D2A2. Most business enterprises have data silos due to a combination of organizational, technical,…
Updated April 17, 2025
Evaluating a Semantic Layer: How Technical Teams Drive ROI Through Smart Data InfrastructurePicking Up From Part 1: Why Technical Evaluation Matters After realizing a semantic layer could solve PrecisionWorks’ biggest data headaches, Alex, a data engineer, moves on to a critical step: evaluating vendors. (Missed the beginning? Catch up on Part 1:…
Updated March 31, 2025
Building Trust in NLQ: How to Improve Cortex Analyst Results in SnowflakeNatural language querying (NLQ) has the potential to make data accessible to everyone — not just analysts and data engineers. But in practice, tools like Snowflake Cortex Analyst often deliver inconsistent or inaccurate results. That inconsistency erodes trust, slows adoption,…
Updated March 6, 2025
Troubleshooting Snowflake Cortex Analyst: Why Your Results Aren’t AccurateGetting inconsistent or inaccurate results from Snowflake Cortex Analyst? You’re not alone. While Cortex Analyst’s text-to-SQL engine often boasts over 90% accuracy, real-world use cases frequently expose gaps in precision, context, and consistency. The culprit — a missing semantic layer.…
Updated February 25, 2025
Announcing the Integration of AtScale and Databricks AI/BI Genie: Redefining Natural Language Query AccuracySummary: AtScale now integrates with Databricks Genie, enabling AI-driven natural language querying (NLQ) with governed, business-defined accuracy. Genie enables business users to query data in plain language, while AtScale ensures AI-generated SQL queries align with business definitions. AtScale’s semantic layer…
Updated January 23, 2025
Using AI for Data AnalyticsAI has become increasingly popular among online users in the past several years. With the rise of generative AI tools like ChatGPT, AI has seemingly taken over the internet. Its use cases seem nearly unlimited, ranging from content generation to…
