Building Time Series Analysis on Snowflake with a Semantic Layer

In a recent post, we discussed how a semantic layer helps scale data science and enterprise AI programs. With massive adoption of Snowflake’s cloud data platform, many organizations are shifting analytics and data science workloads to the Snowflake cloud. Leveraging the integration of AtScale’s semantic layer to Snowflake, data teams can position the data scientist teams to spend less time data wrangling and more time creating sophisticated models that create value for their organizations.

Predictions based on time-series analysis are one of the most common products delivered by data science teams. This post focuses on how AtScale + Snowflake create a powerful combination in this common use case.

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