park SQL is a component of Apache Spark that enables querying structured data using SQL syntax, either through SQL queries or DataFrame APIs. Here’s a brief overview of some basic queries you can perform using Spark SQL:

1. Selecting Data:To select specific columns from a DataFrame:SELECT col1, col2 FROM table_name;
2. Filtering Data:To filter rows based on certain conditions:SELECT * FROM table_name WHERE condition;
3. Aggregating Data:To perform aggregation operations like sum, count, average, etc.:SELECT COUNT(*), AVG(salary) FROM employee_table;
4. Grouping Data:To group data based on certain columns:SELECT department, AVG(salary) FROM employee_table GROUP BY department;
5. Joining Data:To join two or more tables based on a common key:SELECT * FROM table1 JOIN table2 ON table1.key = table2.key;
6. Sorting Data:To sort data based on one or more columns:SELECT * FROM table_name ORDER BY column_name ASC/DESC;
7. Subqueries:To use a query within another query:SELECT * FROM table1 WHERE col1 IN (SELECT col2 FROM table2);
8. Window Functions:To perform calculations across a set of rows:ELECT department, employee_id, salary, AVG(salary) OVER (PARTITION BY department) AS avg_salary_department FROM employee_table;
9. Common Table Expressions (CTEs):To define temporary named result sets for use in a query:WITH cte AS ( SELECT department, AVG(salary) AS avg_salary FROM employee_table GROUP BY department ) SELECT * FROM cte WHERE avg_salary > 50000;
10. Union:To combine the results of two or more SELECT statements:SELECT col1 FROM table1 UNION SELECT col2 FROM table2;

These are some of the basic SQL queries you can perform using Spark SQL. Keep in mind that Spark SQL supports a wide range of SQL functionalities, and you can use it to handle complex data manipulation and analysis tasks.