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- 1. What is a Database?
- 2. What is DBMS?
- 3. What is RDBMS?
- 4. How is data stored in a relational database?
- 5. What is ACID?
- 6. What is SQL?
- 7. What is a subset of the SQL language?
- 8. What are DDL commands?
- 9. What are DQL commands?
- 10. What are DML commands?
- 11. What is index in SQL?
- 12. Index usage
- 13. Is the index fit for query?
- 14. Is the index fit for queries?
- 15. What is a covering index?
- 16. Using a covering index
- 17. What is a constraint in SQL?
- 18. What is a primary key?
- 19. SQL constraints types
- 20. SQL Tables joins types
- 21. Choose join type
- 22. Choose tables join type
- 23. Tables joining algorithms in SQL
- 24. Order of execution of logical operators
- 25. SQL set operators
- 26. Difference between UNION and UNION ALL
- 27. How to find common rows in SQL?
- 28. What relation types exists in SQL?
- 29. Determine the type of relationship
- 30. What is a view in SQL?
- 31. What is a materialized view?
- 32. How avoid accidental deletion?
- 33. What is a SQL transaction?
- 34. What is normalization in SQL?
- 35. What is denormalization in RDB?
- 36. What is a subquery?
- 37. What is a correlated subquery?
- 38. What is "PIVOT" in SQL?
- 39. HAVING without aggregate
- 40. What is FULL-TEXT index?
- 1. Get the actors
- 2. Sort the penguins
- 3. Addresses Lacking Postal Codes
- 4. Ordered Languages List
- 5. Retrieve Actor Names
- 6. Get the languages list
- 7. Ordered Movie Titles
- 8. Get list of clients
- 9. Unique Movie Ratings
- 10. Top 5 Longest Films
- 11. Top 10 Movies by Title
- 12. Get films list third page
- 13. Sort Movies by Multiple Fields
- 14. Get the longest movie
- 15. Find long movies
- 16. Find stuff members by condition
- 17. Find active customers
- 18. Retrieve Actors by Name
- 19. Find film names by description
- 20. Get the sorted list of films with condition
- 21. Find Long Comedies
- 22. Select clients without the letter "A"
- 23. NC-17 Films about DBA
- 24. Films about Dogs or Cats
- 25. Get list of restricted films
- 26. Get the restricted films list
- 27. Employees on the Video Database Project
- 28. Find foreign employees
- 29. Find employees by hire date
- 30. Films Without Available Inventory
- 31. Find languages not represented in films
- 32. List Movies with Their Categories
- 33. Extract address and domain from email
- 34. Get table columns data
- 35. Get list of indexes
- 36. Movies without cast records
- 37. Clients whose first name is the last name of another client
- 38. Find clients who have met each other
- 39. Find movies that have never been rented
- 40. Find films in several categories
- 41. Customers with matching name initials
- 42. Rental history
- 43. Rented Films
- 1. Calculate circle perimeter
- 2. Calculate the area of a circle
- 3. Find the hypotenuse of a triangle
- 4. Calculate factorial
- 5. Generate a list of movies in JSON format
- 6. Addresses with Even Postal Codes
- 7. Build a overal email list
- 8. Generate monthly bill
- 9. Build shared surnames list
- 10. Identify Palindrome Names
- 11. Format Customer Names
- 12. Calculate the tax
- 13. Get formatted list of films
- 14. Find the tomorrow date
- 15. Start and End Dates of Current Month
- 16. Find the first and last dates of week
- 1. Find the average length of a movie
- 2. Find the minimal and maximal film rental cost
- 3. Average Rental Duration
- 4. Find the number of employees
- 5. Find the number of films in each category
- 6. The average cost of renting a movie by category
- 7. Find minimum, maximum and average film duration
- 8. Find long movie categories
- 9. Find the least popular movies
- 10. Find the top spending customers
- 11. Average film rental duration for each customer
- 12. Analyze monthly payment
- 13. Find movie distribution by store
- 14. Find valuable employees
- 15. Salary Ratio Calculation
- 16. Quarterly earnings analysis
- 17. Find the countries with the most customers
- 18. Find the count of rented disks
- 19. Find the number of returns
- 20. Get a list of actors - namesakes
- 21. Get movie cast lists
- 22. Find all the actors in the film
- 23. Analyze weekly rentals
- 24. Find repeat rentals
- 25. Movies in One Store
- 26. Find movies with no available copies
- 27. Distribution of films by categories in JSON format
- 28. Find a June 2005 hit
- 29. Find a 2005 hits
- 30. Analysis of the cost of film rental by category
- 1. Find addresses using sub-query
- 2. Clients who are not familiar with EMILY DEE films
- 3. Movies with the highest replacement cost
- 4. Movies with above-average rental rates
- 5. Clients with a high number of rentals
- 6. Films with Low Rental Time
- 7. Movies without actors records
- 8. Actors Excluding NC-17 Films
- 1. Movie rental prices by category
- 2. Get rolling payment amounts
- 3. Find average disk idle time
- 4. Find the distribution by categories
- 5. Get list of highly paid employees
- 6. Create a salary rating
- 7. Find movie popularity rating
- 8. Find customer details
- 9. Find EMILY DEE fans
- 10. Films with the highest replacement cost
- 11. Find the Horror films fans
- 1. Find the average client activity time
- 2. Find the average revenue
- 3. Find the store average revenue
- 4. Analyze customer payments
- 5. Analyze monthly payment
- 6. Analyze monthly payments (2)
- 7. Find movie popularity rating
- 8. Find the count of rented disks
- 9. Find the number of returns
- 10. Disc rental and return statistics
- 11. Count the rental delays
- 12. Calculate the percentage of delays
- 13. Find the most diverse clients
- 14. Daily income by source
- 15. Find actors duets
- 16. Find film distribution
- 17. Find films that were out of stock
- 18. Payments analyze
- 19. Improve payments analyze
- 20. Distribution of clients by weekday
- 21. Improve distribution of clients by weekday
- 22. Client Rentals by Time of Day
- 23. Find never been delayed films
- 24. Find most delayed films
- 25. Staff performance analysis
- 26. Category popularity analysis
- 27. Gap & Islands problem
- 28. Find customers who sow same films
- 29. Get a list of no-show passengers
- 30. Find average flight occupancy
- 31. Find flight occupancy by fare
- 32. Find the median salary
- 33. Find the median booking amount
- 34. Find the median film's length
- 35. Analyze bill length
- 36. Analyze flipper length
- 37. Most Frequent Co-Purchase
- 38. Most popular products
- 39. Not buyng customers
- 40. Average Sales Delay
- 41. Frequently Purchased Product Pairs
- 42. Sales by Category Percentage
- 43. Product Sales Analysis
- 1. Create new address record
- 2. Update the postal code
- 3. Enter Woodridge postal code
- 4. Update Canadian postal codes
- 5. Enter new staff record
- 6. Create customer addresses view
- 7. Penguins distribution
- 8. Remove customer records
- 9. Create department table
- 10. Alter the staff table
- 11. Perform price update
- 12. Update customer's address
- 13. Adjust the rental cost
- 14. Update replacement cost
- 15. Move film between categories
- 16. Penguins stats table
- 17. Actual statistics
- 18. Actual statistics 2
- 19. Delete records
- 20. Delete employee records
- 21. Delete films records
- 22. Rename the table
- 23. Penguins stats
- 24. Create index
- 25. Create unique index
- 26. Drop table
- 27. Create functional index
- 28. Full-Text Index
- 29. Create Penguins Table
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Based on the penguins table, create a penguins_islands_view view with the fields island - the name of the island and penguins_count - the number of penguins living on it.
Use SQLite syntax to write your answer. Descriptions of tables are provided in the right pane.
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Querynomicon (SQLite)
A compact database for learning the basics of SQL.The Querynomicon database tables:
department - table of departments.
- identDepartment ID
- nameDepartment name
- buildingBuilding name
| ident | name | building |
|---|---|---|
| gen | Genetics | Chesson |
little_penguins - table of little penguins.
- speciesPenguin species
- islandIsland of residence
- bill_length_mmBill length, mm
- bill_depth_mmBill depth, mm
- flipper_length_mmFlipper length, mm
- body_mass_gBody mass, g
- sexSex
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex |
|---|---|---|---|---|---|---|
| Gentoo | Biscoe | 52.1 | 17 | 230 | 5550 | MALE |
penguins - table of penguins.
- speciesPenguin species
- islandIsland of residence
- bill_length_mmBill length, mm
- bill_depth_mmBill depth, mm
- flipper_length_mmFlipper length, mm
- body_mass_gBody mass, g
- sexSex
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex |
|---|---|---|---|---|---|---|
| Gentoo | Biscoe | 52.1 | 17 | 230 | 5550 | MALE |
staff - table of employees.
- identEmployee number
- personalEmployee first name
- familyEmployee last name
- deptDepartment
- ageAge
| ident | personal | family | dept | age |
|---|---|---|---|---|
| 7 | Abram | Chokshi | gen | 23 |
machine - table of machines.
- identMachine ID
- nameMachine name
- detailsJSON with details
| ident | name | details |
|---|---|---|
| 1 | WY401 | {"acquired": "2023-05-01"} |
| 2 | Inphormex | {"acquired": "2021-07-15", "refurbished": "2023-10-22"} |
| 3 | AutoPlate 9000 | {"note": "needs software update"} |
