Sigma on Databricks: End-to-End Best Practices Guide
How do you implement, optimize and operate Sigma as an analytics layer on top of Databricks ? This comprehensive end-to-end guide will show you how. Specifically, we walk you through:
- How to configure your warehouse to ensure the best experience for your end users.
- What is the governance structure, the ideal data architecture for analytics, and the multiple levers for optimizing Databricks.
- What are the best practices for workbooks, datasets, and embedding.
This guide is intended for a technical audience: Data Architects, Platform Architects, Data Engineers, Analytics Engineers, BI Engineers, and so on, and is meant to be used as a reference for anyone deploying Sigma on Databricks for the first time, or looking for guidance when optimizing their data platform.
Check out our blog to learn more: A Practical Guide to Using Sigma with Databricks
We are Sigma.
Sigma is a cloud-native analytics platform that uses a familiar spreadsheet interface to give business users instant access to explore and get insights from their cloud data warehouse. It requires no code or special training to explore billions of rows, augment with new data, or perform “what if” analysis on all data in real-time.