1. Create a table named 'SalesRecord' with columns "City, Country, Items, UnitsSold, UnitPrice, UnitCost, TotalRevenue , TotalCost, Year, Month, Status , Quarter" and data types CHAR(25), CHAR(25), CHAR(25), INT, FLOAT, FLOAT, FLOAT, FLOAT, NT, INT, CHAR(25), INT
2. Insert values into the table (SalesRecord) from the given table below.
3. Write a SQL query to print the cities that are only from France (It could be duplicate)
4. Write a SQL query to print items, country, and unit cost whose unit cost more than 100
5. Write a SQL query to print all records from USA order by 'TotalRevenue' in descending order
6. Write a SQL query to print 'City, Items, UnitsSold and Year' where UnitsSold lies between 3000 and 7000.
7. Write a SQL query to fetch minimum revenue and maximum revenue.
8. Write a SQL query to print top 10 records by 'TotalCost'
| City | Country | Items | Units Sold | Unit Price | Unit Cost | Total Revenue | Total Cost | Year | Month | Status | Quarter |
| NYC | USA | Cosmetics | 8446 | 437.2 | 263.33 | 3692591 | 2224085 | 2014 | 10 | Shipped | 1 |
| Reims | France | Vegetables | 3018 | 154.06 | 90.93 | 464953 | 274427 | 2011 | 11 | Shipped | 2 |
| Paris | France | Baby Food | 1517 | 255.28 | 159.42 | 387260 | 241840 | 2016 | 10 | Shipped | 3 |
| Pasadena | USA | Cereal | 3322 | 205.7 | 117.11 | 683335 | 389039 | 2010 | 4 | Shipped | 3 |
| San Francisco | USA | Fruits | 9845 | 9.33 | 6.92 | 91853.9 | 68127.4 | 2011 | 8 | Shipped | 4 |
| Burlingame | USA | Cereal | 9528 | 205.7 | 117.11 | 1959910 | 1115824 | 2014 | 11 | Shipped | 4 |
| Lille | France | Cereal | 2844 | 205.7 | 117.11 | 585011 | 333061 | 2015 | 3 | Shipped | 4 |
| Bergen | Norway | Clothes | 7299 | 109.28 | 35.84 | 797635 | 261596 | 2012 | 5 | Shipped | 4 |
| San Francisco | USA | Vegetables | 2428 | 154.06 | 90.93 | 374058 | 220778 | 2015 | 1 | Shipped | 4 |
| Paris | France | Vegetables | 4800 | 154.06 | 90.93 | 739488 | 436464 | 2013 | 12 | Shipped | 1 |
| Melbourne | Australia | Clothes | 3012 | 109.28 | 35.84 | 329151 | 107950 | 2015 | 12 | Shipped | 1 |
| NYC | USA | Snacks | 2694 | 152.58 | 97.44 | 411051 | 262503 | 2010 | 2 | Shipped | 2 |
| Newark | USA | Household | 1508 | 668.27 | 502.54 | 1007751 | 757830 | 2016 | 11 | Shipped | 2 |
| Bridgewater | USA | Cosmetics | 4146 | 437.2 | 263.33 | 1812631 | 1091766 | 2015 | 12 | Shipped | 2 |
| Nantes | France | Fruits | 7332 | 9.33 | 6.92 | 68407.6 | 50737.4 | 2011 | 1 | Shipped | 3 |
| Cambridge | USA | Clothes | 4820 | 109.28 | 35.84 | 526730 | 172749 | 2010 | 6 | Shipped | 3 |
| Helsinki | Finland | Office Supplies | 2397 | 651.21 | 524.96 | 1560950 | 1258329 | 2016 | 4 | Shipped | 3 |
| Stavern | Norway | Beverages | 2880 | 47.45 | 31.79 | 136656 | 91555.2 | 2012 | 7 | Shipped | 4 |
| Allentown | USA | Clothes | 1117 | 109.28 | 35.84 | 122066 | 40033.3 | 2014 | 9 | Shipped | 4 |
| NYC | USA | Household | 8989 | 668.27 | 502.54 | 6007079 | 4517332 | 2012 | 8 | Shipped | 4 |
| Salzburg | Austria | Snacks | 407 | 152.58 | 97.44 | 62100.1 | 39658.1 | 2012 | 9 | Shipped | 4 |
| Chatswood | Australia | Clothes | 6313 | 109.28 | 35.84 | 689885 | 226258 | 2010 | 8 | Shipped | 4 |
| Nantes | France | Personal Care | 9681 | 81.73 | 56.67 | 791228 | 548622 | 2011 | 2 | Shipped | 1 |
| New Bedford | USA | Clothes | 515 | 109.28 | 35.84 | 56279.2 | 18457.6 | 2015 | 12 | Shipped | 1 |
| Liverpool | UK | Cereal | 852 | 205.7 | 117.11 | 175256 | 99777.7 | 2012 | 10 | Shipped | 2 |
| Madrid | Spain | Beverages | 9759 | 47.45 | 31.79 | 463065 | 310239 | 2017 | 1 | Disputed | 2 |
| Stavern | Norway | Beverages | 8334 | 47.45 | 31.79 | 395448 | 264938 | 2014 | 10 | Shipped | 1 |
| Lule | Sweden | Fruits | 4709 | 9.33 | 6.92 | 43935 | 32586.3 | 2012 | 1 | Shipped | 1 |
| Madrid | Spain | Meat | 9043 | 421.89 | 364.69 | 3815151 | 3297892 | 2016 | 1 | Shipped | 2 |
| Burlingame | USA | Personal Care | 8529 | 81.73 | 56.67 | 697075 | 483338 | 2016 | 1 | Shipped | 3 |