Blog:
Snowflake

Updated July 25, 2025

As organizations shift their analytics infrastructure to the cloud, many are evaluating whether to replace SQL Server Analysis Services (SSAS) with Snowflake. This analysis compares the two platforms through the lens of semantic modeling — highlighting the strengths and limitations…

Updated July 1, 2025

Many organizations are moving away from legacy SQL Server Analysis Services (SSAS) toward modern, cloud-based platforms like Snowflake. But there's a catch: SSAS isn’t just a storage engine — it’s a powerful semantic layer. Without a plan to replace that…

Updated June 19, 2025

In a multi-tool analytics world, one of the most persistent challenges facing data-driven organizations is metric sprawl. When business teams use different BI tools each with their own logic and metrics, the result is conflicting numbers, duplicated effort, and a…

Updated June 12, 2025

I’ve been attending Snowflake Summits for years, and this year’s event was the most forward-looking I’ve seen. If you’re building for the future of data and AI, the 2025 Snowflake Summit felt like a line in the sand. AI is…

Updated June 5, 2025

Organizations today rely on both Power BI and Tableau to unlock insights from their Snowflake data. But without a centralized approach, things get messy—definitions drift, performance suffers, and costs climb. Despite significant investments in cloud data platforms, data teams still…

Updated May 27, 2025

If your Power BI dashboards feel more like a waiting game than a real-time tool, you're not alone. Many teams struggle with sluggish performance when connecting Power BI to Snowflake. Dashboards that should load in seconds often take minutes. The…

Updated April 15, 2025

Excel remains a go-to tool for business users who want quick, flexible access to data. But when it comes to connecting Excel to Snowflake, the traditional route — via ODBC — can be a technical nightmare. Driver installs, authentication headaches,…

Updated April 7, 2025

As organizations scale their Snowflake deployments to support enterprise-level BI workloads, cost control quickly becomes a top priority. While Snowflake’s pay-per-use model is powerful, it can lead to unexpected expenses when query inefficiencies and overprovisioned compute go unchecked. Read on…

Updated March 31, 2025

Natural 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 13, 2025

Despite heavy investments in Snowflake's modern data platform, many business users still default to Excel for day-to-day analysis. The result? Stale extracts, inconsistent KPIs, and a frustrating disconnect between centralized data and the tools people actually use. The AtScale semantic…

Updated March 6, 2025

Getting 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 19, 2025

Summary AtScale and Snowflake Cortex Analyst have joined forces to enable AI-driven natural language querying (NLQ) on governed semantic models. This integration ensures accurate, consistent SQL generation, aligning AI-driven queries with business definitions. AtScale acts as the governance layer, optimizing…