Databricks primarily uses SQL as its query language, but it extends standard SQL with additional features and functionalities optimized for big data processing and analytics.

Critical aspects of Databricks’ query language include:

• Databricks SQL is built on the foundation of ANSI SQL, a widely used standard. This means that users with SQL experience will find it familiar and easy to use, reducing the learning curve and allowing them to leverage its advanced features quickly. Scalability: Databricks’ query engine is designed to process large datasets efficiently, allowing users to run complex queries on massive amounts of data.
• Databricks is not just a SQL platform; it’s a comprehensive big data processing solution. It’s built on Apache Spark, a powerful open-source framework. This integration is a game-changer, allowing users to tap into Spark’s distributed computing and data manipulation capabilities, enhancing the power and potential of Databricks SQL.Data Sources: Databricks SQL can query data from various sources, including files (like CSV, JSON, and Parquet), databases (like MySQL PostgreSQL), and cloud storage (like Amazon S3 and Azure Blob Storage).
• Optimized Performance: Databricks utilize various optimizations to improve query performance, such as caching, indexing, and query planning.
• Dataframes and SQL: Databricks allow seamless interaction between DataFrames (Spark’s primary data abstraction) and SQL, providing data manipulation and analysis flexibility.