SQL FOR EVERYONE

Demonstrates faang-style sql interview queries: self joins for salary comparison, duplicates removal with group by and dense_rank, department-wise ranking, and multi-ctes for transactions and analytics.

Explore FAANG and MNCs SQL interview questions, covering returning active users, activation rate, supercloud customers, card launches, top drugs, first transactions, and revenue analysis with window functions, joins, and CTEs.

Master sql query writing by identifying losses for CVS Health manufacturers, calculating absolute losses, ranking profitable drugs, and solving multi-table queries, including power creators data.

Explore advanced sql techniques for data transformation and analytics, including pivoting emails to personal/business/recovery columns, segment revenue calculations, year-over-year growth, and in-the-moment user analysis.

Master advanced sql with faang and mnc style challenges, including max-date per user analytics, session type rankings, two-way relationship detection, apple pay merchant volumes, and exclusive staffing analysis.

Explores SQL queries asked in FAANG and MNCs, including reporting yesterday's posts by region, counting distinct posts, and computing login recurrence with lead to two decimals.

Master running totals with sum over partition by and top grades with rank over partition by. Apply window functions and filtering to activity and enrollment data.

Master advanced sql querying used in FAANG and MNC interviews, including calculating the percentage of immediate orders from a delivery table and computing user balances from transactions. Learn to identify actor-director collaborations and analyze cross-table relationships with joins and group by.

Learn to build a page recommendation query by using friends' likes, excluding pages the user has already liked, and returning deduplicated results from friendship and likes tables.

Learn to report the names of sellers who did not make any sales in 2020 by joining customers, orders, and seller tables with a left join and sorting by seller.

Identify salespersons who did not have any order with the red company by left join across salesperson, orders, and company, then exclude those linked to red using except.

Explore sql interview-focused queries from faang and mncs by identifying missing data across employees and salaries tables using left joins and unions, returning missing employee IDs in ascending order.

Master SQL query writing covers calculating team sizes via group by and count on the employee table, and finding students in non-existent departments with a left join and null check.

Learn to compute department-wise maximum salary by joining employee and department tables, and identify day-wise maximum transaction IDs using group by and rank methods, outputting results in ascending transaction IDs.

Master SQL query writing demonstrates solving a three-table problem to find most recent order per product using rank function and partition by product, with joins and sorting by product name.

Master sql query writing - the next level explores faang interview questions on revenue analysis, losses and profits, subqueries, joins, derived tables, ranking functions, and top-n reporting.

Explore advanced SQL with FAANG and MNCS problems, including multi-table joins to identify power creators, transforming emails to separate columns, revenue segmentation, year-over-year growth, and shopping spree detection.

Explore the differences between delete and truncate: delete uses a where clause and manual commits to remove rows; truncate purges all data automatically as a fast DDL operation.

Learn the difference between union and union all: union returns distinct rows, union all returns all rows, with rules for same column count, data type, and order, as set operators.

Compare stored procedures and functions to understand differences in return values and how they execute statements. Learn calling, parameters, exception handling, and transactions, including table variables and temp tables.

Identify and delete duplicate rows by grouping on name, salary, and department with min(id), or by ranking with partition by these columns and keeping the first row.

Learn to compute the third highest department salary in SQL by joining employees and departments, using dense_rank with partition by department, plus derived tables and CTEs.

Compute the top three highest salaries across all departments using partition by and dense_rank, then switch to ranks 1–3 to return across-department results.

Learn how recursive CTEs in SQL reference themselves to process hierarchical data, using an anchor member and union all, illustrated by an employee table with a CEO and escalating levels.

Explain the differences between row_number, rank, and dense_rank in SQL. Use the over clause with order by to apply them; partition by remains optional.

Learn about correlated subqueries in SQL, where the inner query depends on the outer one and is evaluated for each row to enable dynamic, row-by-row filtering.

Explore advanced SQL concepts such as window functions, recursive queries, correlated subqueries, and ranking functions. See practical examples on category sums, over partition by, and performance considerations.

Master practical SQL performance tips to speed data retrieval by selecting only necessary columns, using proper indexing, and choosing efficient joins, unions, and aggregations.