The Practical Guide to Using a Semantic Layer for Data & Analytics

Read about real-life examples and see quantifiable results by leveraging a semantic layer to unlock data for AI & BI at scale.

Practical Guide: How to use a semantic layer for data and analytics - cover

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Download this practitioners’ guide to learn about using a semantic layer to make data accessible to everyone in your organization.

AtScale Diagrams R3 Semantic Layer

Learn how and why the world’s most data-driven organizations use a semantic layer for speed of thought query performance and consistent KPIs across all of their BI/AI tools like Excel, Power BI, Tableau, Looker, Databricks, DataRobot, and H20 using a live data connection to Snowflake, Google BigQuery, Databricks, Amazon Redshift, Microsoft Azure Synapse and more.

Download this guide for practical advice on how to use a semantic layer to unlock data for AI & BI at Scale. You’ll learn how a semantic layer delivers massive ROI with order of magnitude better query performance, concurrency, cost management, and ease of use.

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Read this guide to learn

  • Making better, faster, smarter data-driven decisions at scale using a semantic layer.
  • How a semantic layer delivers massive ROI with order of magnitude better query performance, concurrency, cost management, and ease of use.
  • Enabling data teams to model and deliver a semantic layer on data in the cloud.
  • Achieving speed of thought query performance and consistent KPIs across any BI/AI tool like Excel, Power BI, Tableau, Looker, DataRobot, Databricks and more.
  • Achieving optimal performance on large datasets while improving query performance and user concurrency by 10x.
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Who should read this?

Data & Analytics leaders and practitioners (e.g., Chief Data Officers, data scientists, data literacy, business intelligence, and analytics professionals) looking to leverage a semantic layer to unlock the full value of AI & BI at scale.