AtScale + Databricks Overview

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Learn how the combination of AtScale’s Semantic Layer and Databricks make your data scientists and business intelligence teams more productive using a Lakehouse.

AtScale and Databricks have been working together for years to enable customers the ability to visually model data and provide a “ Diamond Layer” in the Databricks Lakehouse .

In this Tech Talk, David will provide an overview of AtScale + Databricks and dig into popular use cases like replacing Microsoft SQL Server Analysis Services (SSAS) OLAP cubes on the Databricks Lakehouse, eliminating additional data warehouses from a Lakehouse architecture, and providing a live connection with additional dialect support for BI tools like Power BI via DAX.

Before And After AtScale

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In this Tech Talk

We will dive deep on:

  • Eliminating additional data warehouses as a query layer and avoiding unnecessary costs/managerial overhead by delivering all use cases through a simple and unified Lakehouse platform.
  • Replacing legacy “Cube” architectures like SSAS to support a solution that enables blazing fast dimensional analysis by creating aggregates on Databricks.
  • Delivering a “Diamond Layer” for analysis ready data across popular BI, Data Science, and ML services with a semantic layer
  • Extending tooling support through dialect support for DAX, MDX, Python, and SQL

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Who should watch

BI and AI leaders and practitioners (e.g., Chief Data Officers, data scientists, and analytics professionals) who want to better understand how Databricks’s Lake House and AtScale’s Semantic Layer can deliver blazing fast self-service analysis for everyone in your organization.