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Webcast: HOW TO SCALE A DATA LITERACY PROGRAM AT YOUR ORGANIZATION

Webcast: HOW TO SCALE A DATA LITERACY PROGRAM AT YOUR ORGANIZATION

Join data & analytics leaders from Starbucks, Cardinal Health,...

How EverQuote Democratized Data Through Self-Service Analytics

How EverQuote Democratized Data Through Self-Service Analytics

During our recent webinar on scaling self-service analytics, AtScale...

How Analytics Governance Empowers Self-Service BI

How Analytics Governance Empowers Self-Service BI

The benefit of implementing analytics policies at the semantic...

How To Create A Powerful SMS Marketing Strategy

How To Create A Powerful SMS Marketing Strategy

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Emerging Frameworks & Technologies that Combat the Rising Threat of Cyber Attacks

Emerging Frameworks & Technologies that Combat the Rising Threat of Cyber Attacks

The creation of the first computer virus in 1971...

Building Time Series Analysis on Snowflake with a Semantic Layer

September 15, 2021 No Comments

Atscale time series 1 300x170 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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