Query optimization for analytical queries is the process of selecting the most efficient way to execute complex queries that operate on large volumes of data. Analytical queries are commonly used in decision support systems, data warehouses, business intelligence applications, and reporting systems. Unlike transactional queries, which usually access a small number of records and perform frequent updates, analytical queries often involve large scans, multiple joins, aggregations, sorting, grouping, and filtering. Therefore, efficient query optimization is essential for reducing execution time, memory consumption, disk I/O, and overall system cost. The query optimizer is a major component of a Database Management System (DBMS). It examines a SQL query and generates alternative execution plans before selecting an efficient plan. The optimizer considers factors such as table size, available indexes, data distribution, join methods, access paths, CPU cost, and disk I/O. Cost-based optimization is widely used for analytical workloads because it estimates the cost of different execution plans and chooses the plan with the lowest estimated cost. One important technique is predicate pushdown. In this approach, filtering conditions are applied as early as possible during query execution. For example, if an analytical query joins millions of sales records but only requires transactions from a particular year, the DBMS can filter the sales table before performing the join. This reduces the number of rows that subsequent operations must process and can significantly improve performance. Projection pushdown is another useful technique. It involves selecting only the columns required by the query instead of retrieving all columns from a table. Analytical tables may contain many