Data cells and empty file formats offer cheaper storage and lower costs compared to data warehouses. Although the data-driven model separates information processing and storage, it can be said that modern operations have been unsuccessful in providing the data reliability and performance they need for critical analysis. Making the actual data available in the public domain is a time-consuming process, and it is even more difficult with some of the existing weak and cumbersome methods to digest the data in the data set.

Achieving typical “data warehouse analytics” in a data business requires a flexible, high-performance, and reliable query engine. Starburst enables data teams to adopt a modern data-driven engine approach by enabling data teams to adopt a modern data engine approach with live and impactful queries in native cloud data storage. Starburst is based on open-source TRINO, a best-in-class SQL-based MPP query engine for data stored in data volumes. Starburst lakehouse increases productivity and reduces infrastructure costs while providing high timeliness, interoperability and performance. Starburst supports in-house, fully renewable, corporate and hybrid distributions Data Manipulation Language (Data Manipulation Language) and also supports data based writing operations. With Starburst, you gain the ability to melt and combine data from different sources to expand into even more new applications.

What will you learn when you read this document?

• How can you achieve data warehouse-like analytical capabilities in your data business with Starburst?
• What's behind Starburst's impressive performance and flexibility?
• How to combine and enrich data from different sources for comprehensive applications?
• How to integrate data teams with self-service data melting and advanced analytics?
• What security measures are taken to protect your data with Starburst?