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Sigma on Databricks: Cloud-native Analytics Built for Modern Business

Empower your data and business teams to explore, analyze, & decide from live data on the Databricks Lakehouse

Connect to DataBricksFree Trial
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Connect to your Data Lakehouse in seconds and start exploring, analyzing, and reporting on massive live datasets with Sigma

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Sigma + Databricks

Sigma is the only cloud analytics and business intelligence solution for Databricks that empowers business teams to break free from the confines of the dashboard and SQL, explore live data independently, and make better, faster decisions. Together, Sigma and Databricks bring your data to life for your entire organization.

Why Sigma + Databricks?

Reduce Time to Insight

Actively explore your data and conduct
complex "what if" ad hoc analyses. Sigma lets your data and business teams explore live data from Databricks using a familiar spreadsheet interface.

Live Connection to Databricks

Connect to Databricks Lakehouse in seconds using your cloud compute via Classic and Pro or with their cloud compute via Serverless

Work across multiple data sources safely and securely

With Sigma, your data never leaves Databricks. No extracts, ETL processes, or data migration needed. Everything stays where it belongs and all operations are executed inside the Lake house.

With access to Sigma on Databricks’ Lakehouse, data teams can easily make the results of their work digestible for business users, as well as make ad hoc analytics easy and accessible for every user.

Roger Murff
VP of ISV Partners

Hear from our joint customers

Logo of sigma customer greenland
Nan Xiao
Chief Technology Officer

“In the last few years, Greenland has been thoughtful in its approach to its data stack. We needed a platform to bridge the gap between our data warehouse, our data catalog, and, ultimately, the analysis we do on a day-to-day basis. Partnering with Databricks and Sigma allowed us to organize,  explore and analyze our data in order to work efficiently and deliver data outputs for general consumption.”

READ THE FULL STORY

Finance
50-200 employees
Logo of sigma customer FalconX
Swaroop Thimmegowda
Senior Data Scientist

“Using Sigma we were able to conduct exploratory ad hoc analysis on 50M rows of data across five departments easily and efficiently. This helped us reduce our time-to-insight and as an institutional brokerage, time is money.”

Finance
200-500 employees

Additional Resources

Unlocking Cost Savings and Performance Benefits with Sigma on Databricks Serverless

In the ever-evolving realm of data analytics and business intelligence (BI), adaptability and efficiency are paramount. To stay competitive in this data-driven era, organizations are continually seeking ways to optimize their operations while keeping costs in check.

Read More

Bring Your Own Data to Databricks With Sigma Input Tables

Sigma announces support for Input Tables on Databricks. Create or update tables, and edit like a spreadsheet.

Read More

Revolutionizing Retail Operations with Databricks and Sigma

Sigma and Databricks revolutionizes retail demand forecasting, segmentation, price optimization and more.

Read More

Building the Road Map for Real-Time Data and Analytics

Join Fern Halper, TDWI’s VP of Research, in a panel discussion with Databricks and Sigma Computing about how to build the roadmap for real time data and analytics.

Watch now

A Practical Guide to Using Sigma with Databricks

The purpose of this article is to highlight three significant considerations when architecting Databricks for use with an Analytics platform such as Sigma.

Read more

Databricks Partner Connect Walkthrough

This video is a step-by-step guide on how to use Partner Connect from Databricks to connect to Sigma in just a few clicks

Watch now

Sigma on Databricks: End-to-End Best Practices Guide

Find out how you can configure your warehouse to ensure the best experience for your end users, the ideal data architecture for analytics, the multiple levers for optimizing Databricks, and the best practices for workbooks, datasets, and embedding.

Read more
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