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

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

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, such as 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 streamlined query performance, concurrency, cost management, and ease of use.

Read this guide to learn:

  • How to make better, faster, and smarter data-driven decisions at scale using a semantic layer.
  • How a semantic layer delivers massive ROI with streamlined query performance, concurrency, cost management, and ease of use.
  • How to enable data teams to model and deliver a semantic layer on data in the cloud.
  • How to achieve speed of thought query performance and consistent KPIs across any BI/AI tool, such as Excel, Power BI, Tableau, Looker, DataRobot, Databricks and more.
  • How you can reach optimal performance on large datasets while improving query performance and user concurrency by 10x.

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