Apache Druid utilizes a specialized SQL dialect built on Apache Calcite to simplify its native query interface, allowing for efficient data ingestion and query execution. Unlike traditional SQL INSERT operations, Druid is optimized for ingesting large volumes of data from external sources, with a unique focus on time-based partitioning using a special __time column. The ingestion process is enhanced through primary and secondary partitioning, aimed at improving query performance by organizing data into segments based on time intervals and clustering dimensions. To accommodate these requirements, Druid introduces extended SQL syntax with PARTITIONED BY and CLUSTERED BY clauses, as well as a REPLACE statement for updating data within specified time periods. These extensions are integrated into the Calcite framework without altering its core, leveraging JavaCC for parsing and allowing for seamless upgrades. The process involves parsing SQL statements into a syntax tree, optimizing them through relational expressions, and ultimately converting them into native Druid queries for execution, showcasing the system's efficiency in handling complex data transformations and real-time analytics.