Indexes are important data structures in analytical databases because they improve the speed and efficiency of data retrieval. Analytical databases are designed to handle large volumes of historical and aggregated data for reporting, business intelligence, decision-making, and data analysis. Queries in such databases often involve searching, filtering, sorting, grouping, and joining millions or even billions of records. Without suitable indexes, the database may need to scan the entire table to find the required records, resulting in high input/output operations and longer query execution time. An index provides an organized access path to the data, allowing the database management system to locate required records more quickly. An index generally consists of search-key values and pointers to the corresponding records. When a query contains a condition on an indexed attribute, the database can use the index instead of examining every row. For example, consider a Sales table containing millions of transactions with attributes such as Sale_ID, Customer_ID, Product_ID, Sale_Date, and Amount. If an index is created on Sale_Date, a query that retrieves sales made during a particular month can locate the relevant records much faster than a complete table scan. Indexes are particularly useful in analytical workloads because analytical queries frequently access specific subsets of large datasets. They can improve the performance of selection operations, joins, ordering, and grouping. Indexes can also reduce disk I/O because only relevant records need to be accessed. However, indexes require additional storage and may increase the cost of inserting, deleting, and updating records because the index must also be maintained. Therefore, indexes should be designed according to the workload and query patterns. A primary index is an index created on the primary key of a table. The primary key uniquely identifies every record, so the search-key values in a primary index are unique. In many traditional