What are the most common SQL functions and operators for big data analysis?

ROLLUP and CUBE are used with the GROUP BY clause. ROLLUP calculates subtotals and grand totals. The CUBE is similar in functionality to the ROLLUP , however, the CUBE can calculate subtotals and grand totals for all combinations of the grouping columns.

Here are the most common SQL functions: 1. Aggregate Functions: SUM, AVG, COUNT, MIN, MAX for summarizing data. 2. Window Functions: ROW_NUMBER, RANK, LEAD, LAG for advanced real-time analytical queries. 3. String Functions: CONCAT, SUBSTRING, UPPER, LOWER for manipulating text data. 4.Date and Time Functions: DATE_FORMAT, DATEDIFF, DATE_ADD, DATE_TRUNC for handling temporal data. 5. Logical and Comparison Operators: AND, OR, NOT, =, !=, <, >, BETWEEN for conditional logic. If using an AWS Ecosystem; leverage Amazon Redshift. It supports all of the above functions and provides a scalable environment for efficient Big Data analytics workloads.

Some other key aggregation functions are MAX, MIN, APPROX_PERCENTILE, etc. These can come in handy when trying to get the first last or nth percentile records too.

Another important concept to know here is partitioning which allows you to apply window functions on a selection of rows the range of which can be defined through arguments like bounded/unbounded and preceding/succeeding. Partitioning is an extremely powerful concept and can help you perform many complex operations in SQL. Another key point to note here is the difference between rank, dense rank and row_number. For example, let's say there are 3 student in a class who secure 40, 40 and 30 marks on a test. Their ranks will be 1, 1, 3. Their dense ranks will be 1, 1, 2. Their row numbers will be 1, 2, 3.

SQL also provides the ability to use regexes for string manipulation and the function name can vary between different SQL implementations like prestoSQL, MySQL, etc. But regex based string manipulation is an extremely powerful tool which allows you to search, replace and perform other operations on strings.

NOW() - returns current time CURDATE() - returns current date DATEADD() - adds specified number of date to specified date, similarly DATESUB() to subtract and DATEDIFF() to find number of days between two dates

Another set of important date functions are DATEADD and INTERVAL which can be used to add or subtract a fixed number of days from a particular date. DATEDIFF on the other hand allows you to see the difference in terms days, months, etc. between two dates.

Grouping functions such as GROUP BY & HAVING are essential for aggregating and summarising data at different levels of granularity in SQL queries. Analysts use grouping functions to group data based on one or more columns and apply aggregate functions to each group. Personally, I've used GROUP BY and HAVING clauses in SQL queries to aggregate sales data by product category, region & time period, generating actionable insights for inventory management and sales forecasting in retail analytics. Set operators such as UNION, INTERSECT, and EXCEPT enable analysts to combine and compare datasets from multiple sources or queries in SQL. Analysts use set operators to perform set operations such as union, intersection, and set difference.

In addition to the basic SQL functions and operators, consider the importance of optimization techniques for handling large datasets. Knowing how to write efficient SQL queries that leverage indexing, partitioning, and proper join strategies can significantly improve performance when analyzing big data. Also, be aware of the specific capabilities and extensions of SQL offered by different big data platforms like Apache Hive, Google BigQuery, or Amazon Redshift, which may include custom functions and operators designed to handle big data workloads more effectively. Understanding the nuances of SQL on these platforms can greatly enhance your data analysis efficiency and effectiveness.