Grouping and aggregation functions?

Grouping and aggregation functions are essential for summarizing and analyzing data. GROUP BY clauses partition data into distinct groups based on specified columns, then apply aggregate functions (like SUM, AVG, COUNT, MIN, MAX) to reduce multiple rows within each group into a single summary row. Window functions, conversely, perform calculations over a defined "window" of rows (OVER (PARTITION BY ... ORDER BY ...)) without collapsing the original rows, enabling operations like running totals or rankings.

GROUP BY is used when you need a single summary row for each unique combination of grouping columns, for example, total sales per product category or average user activity per day. The aggregate functions then operate on these logical partitions. Advanced SQL features like GROUPING SETS, CUBE, and ROLLUP (available in systems like Snowflake, Spark SQL) extend GROUP BY to generate multiple aggregation levels or subtotals within a single query, useful for comprehensive analytical reports.

For optimal performance in production, always filter data before grouping using a WHERE clause. This significantly reduces the dataset size and the computational overhead, especially in distributed systems like Spark where GROUP BY can trigger expensive data shuffles across partitions. The HAVING clause is then used to filter groups after aggregation, based on the results of the aggregate functions.

SELECT
    product_category,
    COUNT(DISTINCT customer_id) AS unique_customers,
    SUM(sales_amount) AS total_sales
FROM
    transactions
WHERE
    transaction_date >= '2023-01-01' -- Filter rows before grouping
GROUP BY
    product_category
HAVING
    SUM(sales_amount) > 10000; -- Filter groups after aggregation

In the interview, also mention how understanding data distribution and system architecture (e.g., Snowflake's micro-partitions, Spark's shuffle behavior) is crucial for optimizing aggregation queries on large datasets.