Data analytics has revolutionized the way businesses operate and compete, enabling them to make informed decisions and innovate their products and services. As data volumes grow, so does the need for efficient data processing and analytics tools. With the integration of Sigma on Databricks, business users can now leverage the rich robust data available in Databricks with a familiar spreadsheet user interface that improves overall collaboration between data teams and business analysts and works across multiple data sources safely and securely. This blog explores how these two platforms integrate through Databricks Partner Connect to create a seamless data analytics workflow.
Sigma is a cloud-native analytics platform that provides a familiar front-end user interface enabling you to query, profile, visualize, and explore massive datasets stored and shared in leading data platforms like Databricks. Sigma directly connects to Databricks and a proprietary SQL-generation engine that translates user interactions on a familiar spreadsheet interface into machine-optimized SQL. Sigma unlocks the power of the Databricks Lakehouse Platform by providing speed, scalability and security with the flexibility to pivot billions of rows of data.
Getting started is simple, all you will need is a Databricks account.
Navigate to your instance of Databricks and follow these steps:
Once your Databricks Lakehouse is connected, it's time to start exploring & analyzing your data. Here are some of the ways Sigma makes it easy:
We're excited to be offering the best of both Sigma and Databricks Lakehouse with this easy to use integration!
Integrating Sigma with Databricks through Partner Connect takes this one step further by providing users with a powerful data analytics workflow. Users leverage Databricks' data engineering and machine learning tools to prepare data and train models for Sigma to visualize and analyze. The ability to have Sigma read directly from the Databricks Lakehouse eliminates the need for manual data extracts, reducing the risk of errors and saving time.
To learn more about Sigma on Databricks, please download the Sigma on Databricks Best Practices Guide or visit our Sigma+Databricks page.