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Best Practices for Setting Up Databricks SQL at Enterprise Scale


​This is Johann from the Global Engineering Department of the GLB Division.

​ Today, I had the opportunity to watch a presentation on best practices for leveraging Databricks SQL at an enterprise scale. This presentation explained how to effectively utilize Databricks SQL in setting up a data warehouse. ​ This blog post is part 1 of a series, and the target audience includes business professionals interested in data analysis and business intelligence, data architects and engineers looking for cloud-based data warehouses, and data analysts and engineers using the Databricks Lakehouse platform. ​ Let's dive into how to make the most of Databricks SQL! ​

The Importance of Data and Utilizing TPC Tools

​ Data is an extremely important element in modern business, and companies can enhance their competitiveness by effectively utilizing data. Setting up a data warehouse is crucial for this purpose. In the presentation, best practices for setting up an enterprise-scale data warehouse using Databricks SQL were introduced. ​ To evaluate the performance of a data warehouse, benchmark tools provided by the Transaction Processing Performance Council (TPC) can be helpful. These tools allow for an objective evaluation of data warehouse performance. ​

Overview and Features of Databricks SQL

​ Databricks SQL is a tool that demonstrates excellent performance in setting up a data warehouse. Here are its main features: ​

  1. Fast query execution: Databricks SQL is characterized by its extremely fast query execution speed for data warehouses. This allows business users to quickly perform data analysis.

  2. Security: Databricks SQL is designed with data security in mind, allowing companies to operate their data warehouses with confidence.

  3. Scalability: Databricks SQL supports enterprise-scale data warehouse configurations and can efficiently process large amounts of data.

Case Studies of Adopting Companies (Johnson & Johnson, Atlassian, AB Enamro)

​ Databricks SQL has been adopted by many companies, demonstrating its effectiveness. Below are case studies of adopting companies introduced in the presentation. ​

Johnson & Johnson

​ Johnson & Johnson, a leading company in the medical and pharmaceutical industry, has set up a data warehouse using Databricks SQL, streamlining data analysis. This has enabled the company to identify new business opportunities and enhance its competitiveness. ​


​ Atlassian, a provider of software development tools, has built a data warehouse using Databricks SQL to analyze customer data. This has allowed the company to provide services tailored to customer needs, improving customer satisfaction. ​

AB Enamro

​ In the financial industry, AB Enamro has streamlined its data warehouse configuration and improved data analysis speed by adopting Databricks SQL. This has enabled the company to perform real-time data analysis and make faster decisions. ​


​ Databricks SQL is a powerful tool for setting up enterprise-scale data warehouses, and it has been adopted by many companies. In today's business environment, where the importance of data is increasing, leveraging Databricks SQL can help streamline data warehouse configurations and enhance competitiveness.


This content based on reports from members on site participating in DAIS sessions. During the DAIS period, articles related to the sessions will be posted on the special site below, so please take a look.

Translated by Johann


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