Task: equi-join-detect (source dataset: Auto-BI)

Your are given a database with related tables used for business intelligence analysis. Your task is to identify all key/foreign-key join relationships between the tables for this database. Recall that a join is a relationship between two tables, that allows rows from one table to be combined with rows from another, based on a set of common join columns.
 
 
No explanation needed. Please return all join relationships in the following JSON format:
{
  "joins": [
    {
      "from_table": "table-1",
      "from_column": "column-1",
      "to_table": "table-2",
      "to_column": "column-3"
    }, 
	...
  ]
}

Tables:

Table: 20Dates
| Date                    |   Year |   QuarterOfYear |   MonthOfYear |   DayOfMonth |   DateInt | MonthName   | MonthInCalendar   | QuarterInCalendar   |   DayInWeek | DayOfWeekName   | WeekEnding              |   Week Number |   MonthnYear |   QuarternYear |   ShortYear | FY   | ShrtMnth   |
|:------------------------|-------:|----------------:|--------------:|-------------:|----------:|:------------|:------------------|:--------------------|------------:|:----------------|:------------------------|--------------:|-------------:|---------------:|------------:|:-----|:-----------|
| 2020-07-01 00:00:00.000 |   2020 |               3 |             7 |            1 |  20200701 | July        | Jul 2020          | Q3 2020             |           3 | Wednesday       | 2020-07-04 00:00:00.000 |            27 |     20200700 |       20200300 |          20 | FY21 | JUL        |
| 2020-07-02 00:00:00.000 |   2020 |               3 |             7 |            2 |  20200702 | July        | Jul 2020          | Q3 2020             |           4 | Thursday        | 2020-07-04 00:00:00.000 |            27 |     20200700 |       20200300 |          20 | FY21 | JUL        |
| 2020-07-03 00:00:00.000 |   2020 |               3 |             7 |            3 |  20200703 | July        | Jul 2020          | Q3 2020             |           5 | Friday          | 2020-07-04 00:00:00.000 |            27 |     20200700 |       20200300 |          20 | FY21 | JUL        |
| 2020-07-04 00:00:00.000 |   2020 |               3 |             7 |            4 |  20200704 | July        | Jul 2020          | Q3 2020             |           6 | Saturday        | 2020-07-04 00:00:00.000 |            27 |     20200700 |       20200300 |          20 | FY21 | JUL        |
| 2020-07-05 00:00:00.000 |   2020 |               3 |             7 |            5 |  20200705 | July        | Jul 2020          | Q3 2020             |           0 | Sunday          | 2020-07-11 00:00:00.000 |            28 |     20200700 |       20200300 |          20 | FY21 | JUL        |
| 2020-07-06 00:00:00.000 |   2020 |               3 |             7 |            6 |  20200706 | July        | Jul 2020          | Q3 2020             |           1 | Monday          | 2020-07-11 00:00:00.000 |            28 |     20200700 |       20200300 |          20 | FY21 | JUL        |
| 2020-07-07 00:00:00.000 |   2020 |               3 |             7 |            7 |  20200707 | July        | Jul 2020          | Q3 2020             |           2 | Tuesday         | 2020-07-11 00:00:00.000 |            28 |     20200700 |       20200300 |          20 | FY21 | JUL        |
| 2020-07-08 00:00:00.000 |   2020 |               3 |             7 |            8 |  20200708 | July        | Jul 2020          | Q3 2020             |           3 | Wednesday       | 2020-07-11 00:00:00.000 |            28 |     20200700 |       20200300 |          20 | FY21 | JUL        |
| 2020-07-09 00:00:00.000 |   2020 |               3 |             7 |            9 |  20200709 | July        | Jul 2020          | Q3 2020             |           4 | Thursday        | 2020-07-11 00:00:00.000 |            28 |     20200700 |       20200300 |          20 | FY21 | JUL        |
| 2020-07-10 00:00:00.000 |   2020 |               3 |             7 |           10 |  20200710 | July        | Jul 2020          | Q3 2020             |           5 | Friday          | 2020-07-11 00:00:00.000 |            28 |     20200700 |       20200300 |          20 | FY21 | JUL        |


Table: Annual Brand forecast
| Brand       |   Tot Ret $ |   Tot Qty |
|:------------|------------:|----------:|
| BuildNow    |       25000 |      5000 |
| LookSharp   |     1200000 |    180000 |
| Newbi       |       40000 |     20000 |
| SmoothDrive |     1000000 |    266667 |
| Stealth     |      925000 |     63068 |
| Wheelers    |      350000 |     70000 |
| ZEGO        |     2200000 |    178378 |
| ZORVEL      |      145000 |      2146 |


Table: BOYFCTRACKER



Table: Buyer-Category
| Buyer   |   Buyer-ID | Retailer   | Category        |
|:--------|-----------:|:-----------|:----------------|
| Dave    |          1 | Cost Plus  | Collectables    |
| Pete    |          2 | Cost Plus  | Construction    |
| Tom     |          3 | Cost Plus  | Dress Up        |
| Will    |          4 | Cost Plus  | Vehicles        |
| Sally   |          5 | Cost Plus  | Blasters        |
| Sandra  |          6 | Cost Plus  | Preschool       |
| Saki    |          7 | Cost Plus  | Miniature Dolls |
| Lauren  |          8 | Cost Plus  | Tech Toys       |
| Barbara |          9 | Cost Plus  | Ride Ons        |
| George  |         10 | Cost Plus  | Seasonal        |


Table: Calendar
| Date                    |   Year |   Week_No | Day Name   |   Year.1 |   Month | Month Name   | Quarter   |   Week of Year | Start of Week           | End of Month            | UPPERCASE   |   Day Inx |   YM Key |   Last Sale Date |
|:------------------------|-------:|----------:|:-----------|---------:|--------:|:-------------|:----------|---------------:|:------------------------|:------------------------|:------------|----------:|---------:|-----------------:|
| 2018-07-01 00:00:00.000 |   2018 |        27 | Sunday     |     2018 |       7 | July         | Q3        |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |       223 |   201807 |                0 |
| 2018-07-02 00:00:00.000 |   2018 |        27 | Monday     |     2018 |       7 | July         | Q3        |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |       224 |   201807 |                0 |
| 2018-07-03 00:00:00.000 |   2018 |        27 | Tuesday    |     2018 |       7 | July         | Q3        |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |       225 |   201807 |                0 |
| 2018-07-04 00:00:00.000 |   2018 |        27 | Wednesday  |     2018 |       7 | July         | Q3        |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |       226 |   201807 |                0 |
| 2018-07-05 00:00:00.000 |   2018 |        27 | Thursday   |     2018 |       7 | July         | Q3        |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |       227 |   201807 |                0 |
| 2018-07-06 00:00:00.000 |   2018 |        27 | Friday     |     2018 |       7 | July         | Q3        |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |       228 |   201807 |                0 |
| 2018-07-07 00:00:00.000 |   2018 |        27 | Saturday   |     2018 |       7 | July         | Q3        |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |       229 |   201807 |                0 |
| 2018-07-08 00:00:00.000 |   2018 |        28 | Sunday     |     2018 |       7 | July         | Q3        |             28 | 2018-07-08 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |       230 |   201807 |                0 |
| 2018-07-09 00:00:00.000 |   2018 |        28 | Monday     |     2018 |       7 | July         | Q3        |             28 | 2018-07-08 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |       231 |   201807 |                0 |
| 2018-07-10 00:00:00.000 |   2018 |        28 | Tuesday    |     2018 |       7 | July         | Q3        |             28 | 2018-07-08 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |       232 |   201807 |                0 |


Table: Cat _URL
| Category     | Web URL                                                                                                                                               |
|:-------------|:------------------------------------------------------------------------------------------------------------------------------------------------------|
| Blasters     | https://images-na.ssl-images-amazon.com/images/I/71zZLhe3oSL._AC_UL160_.jpg                                                                           |
| Collectables | https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcT8O6EZ3xChp8Ef_WZ4hgLA82LLoqURO-23eBdwOXgqP9MGRaZk                                              |
| Construction | https://target.scene7.com/is/image/Target/GUEST_84bd4778-a5e0-4795-969d-670f5761cca1?wid=325&hei=325&qlt=80&fmt=webp                                  |
| Dress Up     | https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQmV8eWEKrvmKZnC3FNY32jE5Q_Ui3Q17VHOcrguchrlDhDckqT                                              |
| Mini Dolls   | https://target.scene7.com/is/image/Target/GUEST_635435c5-40d8-4679-897e-d110cab3cb8e?wid=325&hei=325&qlt=80&fmt=webp                                  |
| PreSchool    | https://i5.walmartimages.com/asr/a169d8ba-9b39-4910-87be-111d964d83fe_1.96fc998f638e4d08de3f4ffe4c06cdd0.jpeg?odnWidth=200&odnHeight=200&odnBg=ffffff |
| Ride On's    | https://i5.walmartimages.com/asr/bf836c25-ffa7-4fc0-8888-5897309f9b34_2.a23dc50b2b79378bb317785411768546.jpeg?odnWidth=200&odnHeight=200&odnBg=ffffff |
| Seasonal     | https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ508HIeNucx24oVirarQXX7u9hb5ErhWmvSRoGHGa88xu9a10HdA                                            |
| Tech Toys    | https://m.media-amazon.com/images/S/gladiator-image-upload-prod/5/ATVPDKIKX0DER/819754b42a04ec84c77149dedc7e64b6._AC_SR218,200_SX200_SY200_.jpg       |
| Vehicles     | https://m.media-amazon.com/images/I/61JUqaMIufL._AC_UL480_FMwebp_QL65_.jpg                                                                            |


Table: Customers
|   Store Index | Retail     |   Retail_idx | City             |   Zip |   Zip_ID |   Store_Sq_Ft | Sales_Person   |
|--------------:|:-----------|-------------:|:-----------------|------:|---------:|--------------:|:---------------|
|             1 | Top Dollar |            3 | ATHERTON         | 94027 |        1 |          1000 | Will S         |
|             2 | Top Dollar |            3 | MANALAPAN        | 33462 |        2 |          1000 | Will S         |
|             3 | Top Dollar |            3 | LOS ALTOS        | 94022 |        3 |          1000 | Will S         |
|             4 | Top Dollar |            3 | PALO ALTO        | 94301 |        4 |          1000 | Will S         |
|             5 | Top Dollar |            3 | ROSS             | 94957 |        5 |          1000 | Joe B          |
|             6 | Top Dollar |            3 | SAGAPONACK       | 11962 |        6 |          1000 | Joe B          |
|             7 | Top Dollar |            3 | WOODY CREEK      | 81656 |        7 |          1000 | Joe B          |
|             8 | Top Dollar |            3 | BEVERLY HILLS    | 90210 |        8 |          1000 | Joe B          |
|             9 | Top Dollar |            3 | LONGMEADOW       |  1106 |        9 |          1000 | Bill S         |
|            10 | Top Dollar |            3 | COLORADO SPRINGS | 80922 |       10 |          1000 | Jim S          |


Table: Dates Alt
| Date                    |   day |   month |   year |   day index |   week index |   month index |   year index |
|:------------------------|------:|--------:|-------:|------------:|-------------:|--------------:|-------------:|
| 2021-01-01 00:00:00.000 |     1 |       1 |   2021 |        -555 |          -79 |           -18 |           -1 |
| 2021-01-02 00:00:00.000 |     2 |       1 |   2021 |        -554 |          -79 |           -18 |           -1 |
| 2021-01-03 00:00:00.000 |     3 |       1 |   2021 |        -553 |          -79 |           -18 |           -1 |
| 2021-01-04 00:00:00.000 |     4 |       1 |   2021 |        -552 |          -79 |           -18 |           -1 |
| 2021-01-05 00:00:00.000 |     5 |       1 |   2021 |        -551 |          -79 |           -18 |           -1 |
| 2021-01-06 00:00:00.000 |     6 |       1 |   2021 |        -550 |          -78 |           -18 |           -1 |
| 2021-01-07 00:00:00.000 |     7 |       1 |   2021 |        -549 |          -78 |           -18 |           -1 |
| 2021-01-08 00:00:00.000 |     8 |       1 |   2021 |        -548 |          -78 |           -18 |           -1 |
| 2021-01-09 00:00:00.000 |     9 |       1 |   2021 |        -547 |          -78 |           -18 |           -1 |
| 2021-01-10 00:00:00.000 |    10 |       1 |   2021 |        -546 |          -78 |           -18 |           -1 |


Table: Dates
| Date                    |   Year |   QuarterOfYear |   MonthOfYear |   DayOfMonth |   DateInt | MonthName   | MonthInCalendar   | QuarterInCalendar   |   DayInWeek | DayOfWeekName   | WeekEnding              |   Week Number |   MonthnYear |   QuarternYear |   ShortYear | FY   |
|:------------------------|-------:|----------------:|--------------:|-------------:|----------:|:------------|:------------------|:--------------------|------------:|:----------------|:------------------------|--------------:|-------------:|---------------:|------------:|:-----|
| 2015-07-01 00:00:00.000 |   2015 |               3 |             7 |            1 |  20150701 | July        | Jul 2015          | Q3 2015             |           3 | Wednesday       | 2015-07-04 00:00:00.000 |            27 |     20150700 |       20150300 |          15 | FY16 |
| 2015-07-02 00:00:00.000 |   2015 |               3 |             7 |            2 |  20150702 | July        | Jul 2015          | Q3 2015             |           4 | Thursday        | 2015-07-04 00:00:00.000 |            27 |     20150700 |       20150300 |          15 | FY16 |
| 2015-07-03 00:00:00.000 |   2015 |               3 |             7 |            3 |  20150703 | July        | Jul 2015          | Q3 2015             |           5 | Friday          | 2015-07-04 00:00:00.000 |            27 |     20150700 |       20150300 |          15 | FY16 |
| 2015-07-04 00:00:00.000 |   2015 |               3 |             7 |            4 |  20150704 | July        | Jul 2015          | Q3 2015             |           6 | Saturday        | 2015-07-04 00:00:00.000 |            27 |     20150700 |       20150300 |          15 | FY16 |
| 2015-07-05 00:00:00.000 |   2015 |               3 |             7 |            5 |  20150705 | July        | Jul 2015          | Q3 2015             |           0 | Sunday          | 2015-07-11 00:00:00.000 |            28 |     20150700 |       20150300 |          15 | FY16 |
| 2015-07-06 00:00:00.000 |   2015 |               3 |             7 |            6 |  20150706 | July        | Jul 2015          | Q3 2015             |           1 | Monday          | 2015-07-11 00:00:00.000 |            28 |     20150700 |       20150300 |          15 | FY16 |
| 2015-07-07 00:00:00.000 |   2015 |               3 |             7 |            7 |  20150707 | July        | Jul 2015          | Q3 2015             |           2 | Tuesday         | 2015-07-11 00:00:00.000 |            28 |     20150700 |       20150300 |          15 | FY16 |
| 2015-07-08 00:00:00.000 |   2015 |               3 |             7 |            8 |  20150708 | July        | Jul 2015          | Q3 2015             |           3 | Wednesday       | 2015-07-11 00:00:00.000 |            28 |     20150700 |       20150300 |          15 | FY16 |
| 2015-07-09 00:00:00.000 |   2015 |               3 |             7 |            9 |  20150709 | July        | Jul 2015          | Q3 2015             |           4 | Thursday        | 2015-07-11 00:00:00.000 |            28 |     20150700 |       20150300 |          15 | FY16 |
| 2015-07-10 00:00:00.000 |   2015 |               3 |             7 |           10 |  20150710 | July        | Jul 2015          | Q3 2015             |           5 | Friday          | 2015-07-11 00:00:00.000 |            28 |     20150700 |       20150300 |          15 | FY16 |


Table: Forecast
| Customer   |   Cust_Inx |   Year | Date                    | Month   |   Forecast_Amt |
|:-----------|-----------:|-------:|:------------------------|:--------|---------------:|
| Cost Plus  |          1 |   2018 | 2018-01-01 00:00:00.000 | Jan     |         149207 |
| Cost Plus  |          1 |   2018 | 2018-02-01 00:00:00.000 | Feb     |         106483 |
| Cost Plus  |          1 |   2018 | 2018-03-01 00:00:00.000 | Mar     |         116975 |
| Cost Plus  |          1 |   2018 | 2018-04-01 00:00:00.000 | Apr     |         151918 |
| Cost Plus  |          1 |   2018 | 2018-05-01 00:00:00.000 | May     |         132658 |
| Cost Plus  |          1 |   2018 | 2018-06-01 00:00:00.000 | Jun     |         129408 |
| Cost Plus  |          1 |   2018 | 2018-07-01 00:00:00.000 | Jul     |         169690 |
| Cost Plus  |          1 |   2018 | 2018-08-01 00:00:00.000 | Aug     |         126517 |
| Cost Plus  |          1 |   2018 | 2018-09-01 00:00:00.000 | Sep     |         163856 |
| Cost Plus  |          1 |   2018 | 2018-10-01 00:00:00.000 | Oct     |         470580 |


Table: Metric Selection
| Metric       |   Index |
|:-------------|--------:|
| Revenue      |       1 |
| Costs        |       2 |
| Gross Margin |       3 |
| Units        |       4 |


Table: Monthly FCast by Brand
| Brand    | Month     |   Seasonality Factor |   Tot Ret $ Annual |   Tot Qty Annual |   $ Fcast Monthly Brand |   Brand Qty FC |   Index |
|:---------|:----------|---------------------:|-------------------:|-----------------:|------------------------:|---------------:|--------:|
| BuildNow | January   |                 0.02 |              25000 |             5000 |                     500 |            100 |       1 |
| BuildNow | February  |                 0.04 |              25000 |             5000 |                    1000 |            200 |       2 |
| BuildNow | March     |                 0.05 |              25000 |             5000 |                    1250 |            250 |       3 |
| BuildNow | April     |                 0.05 |              25000 |             5000 |                    1250 |            250 |       4 |
| BuildNow | May       |                 0.05 |              25000 |             5000 |                    1250 |            250 |       5 |
| BuildNow | June      |                 0.03 |              25000 |             5000 |                     750 |            150 |       6 |
| BuildNow | July      |                 0.04 |              25000 |             5000 |                    1000 |            200 |       7 |
| BuildNow | August    |                 0.06 |              25000 |             5000 |                    1500 |            300 |       8 |
| BuildNow | September |                 0.1  |              25000 |             5000 |                    2500 |            500 |       9 |
| BuildNow | October   |                 0.12 |              25000 |             5000 |                    3000 |            600 |      10 |


Table: New Measures
| Column1      |
|:-------------|
| new measures |


Table: Order-Calendar
| Date                    |   Year |   Week_No | Day Name   |   Year.1 |   Month | Month Name   |   Quarter |   Week of Year | Start of Week           | End of Month            | UPPERCASE   |
|:------------------------|-------:|----------:|:-----------|---------:|--------:|:-------------|----------:|---------------:|:------------------------|:------------------------|:------------|
| 2018-07-01 00:00:00.000 |   2018 |        27 | Sunday     |     2018 |       7 | July         |         3 |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |
| 2018-07-02 00:00:00.000 |   2018 |        27 | Monday     |     2018 |       7 | July         |         3 |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |
| 2018-07-03 00:00:00.000 |   2018 |        27 | Tuesday    |     2018 |       7 | July         |         3 |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |
| 2018-07-04 00:00:00.000 |   2018 |        27 | Wednesday  |     2018 |       7 | July         |         3 |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |
| 2018-07-05 00:00:00.000 |   2018 |        27 | Thursday   |     2018 |       7 | July         |         3 |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |
| 2018-07-06 00:00:00.000 |   2018 |        27 | Friday     |     2018 |       7 | July         |         3 |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |
| 2018-07-07 00:00:00.000 |   2018 |        27 | Saturday   |     2018 |       7 | July         |         3 |             27 | 2018-07-01 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |
| 2018-07-08 00:00:00.000 |   2018 |        28 | Sunday     |     2018 |       7 | July         |         3 |             28 | 2018-07-08 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |
| 2018-07-09 00:00:00.000 |   2018 |        28 | Monday     |     2018 |       7 | July         |         3 |             28 | 2018-07-08 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |
| 2018-07-10 00:00:00.000 |   2018 |        28 | Tuesday    |     2018 |       7 | July         |         3 |             28 | 2018-07-08 00:00:00.000 | 2018-07-31 00:00:00.000 | JUL         |


Table: Products
|   Category |   Item# |   Item_Index | Descrition             | Brand   | SubCategory   |   Retail Price |   WhslCost_Unit |   FactoryCost |
|-----------:|--------:|-------------:|:-----------------------|:--------|:--------------|---------------:|----------------:|--------------:|
|        107 |    1012 |            1 | DeepFake               | ZEGO    | AI            |          99.99 |           62.99 |         31.5  |
|        107 |    1013 |            2 | AI coder               | ZEGO    | AI            |          59.99 |           37.79 |         18.9  |
|        107 |    1014 |            3 | Facial Recognition     | ZEGO    | AI            |         149.99 |           94.49 |         47.25 |
|        107 |    1015 |            4 | EV Train               | ZEGO    | AV            |          49.99 |           20    |         10    |
|        107 |    1016 |            5 | RoboMan                | ZEGO    | Robotic       |          49.99 |           31.49 |         15.75 |
|        107 |    1017 |            6 | RoboWoman              | ZEGO    | Robotic       |          49.99 |           20    |         10    |
|        100 |    2214 |            7 | Amazing Snake Boy      | ZORVEL  | Comic         |           9.99 |            4    |          1.6  |
|        100 |    2215 |            8 | Fighters from the East | ZORVEL  | Figures       |           9.99 |            4    |          1.6  |
|        100 |    2216 |            9 | Fighters from the West | ZORVEL  | Figures       |           9.99 |            4    |          1.6  |
|        100 |    2217 |           10 | Revengers              | ZORVEL  | Comic         |          12.99 |            5.2  |          2.08 |


Table: Region
| Continent     | Country   | City             |   Zip |   Zip_ID | State   | Sales_Region   |
|:--------------|:----------|:-----------------|------:|---------:|:--------|:---------------|
| North America | USA       | ATHERTON         | 94027 |        1 | CA      | West           |
| North America | USA       | MANALAPAN        | 33462 |        2 | FL      | S.East         |
| North America | USA       | LOS ALTOS        | 94022 |        3 | CA      | West           |
| North America | USA       | PALO ALTO        | 94301 |        4 | CA      | West           |
| North America | USA       | ROSS             | 94957 |        5 | CA      | West           |
| North America | USA       | SAGAPONACK       | 11962 |        6 | NY      | East           |
| North America | USA       | WOODY CREEK      | 81656 |        7 | CO      | West           |
| North America | USA       | BEVERLY HILLS    | 90210 |        8 | CA      | West           |
| North America | USA       | LONGMEADOW       |  1106 |        9 | MA      | East           |
| North America | USA       | COLORADO SPRINGS | 80922 |       10 | CO      | West           |


Table: Sales
| Order_Date              | Ship_Date               |   Month# | Year                    |   Zip ID |   Store_Index |   Buyer_ID | Retailer   |   Ret_Index |   Zip Code |   Item# |   Item_Index |   Cat Code |   Qty_Sold |   SRP | Promo SP   | PrcPt   |   Ret Sls $Amt |   Ship Days |   Service Level % |
|:------------------------|:------------------------|---------:|:------------------------|---------:|--------------:|-----------:|:-----------|------------:|-----------:|--------:|-------------:|-----------:|-----------:|------:|:-----------|:--------|---------------:|------------:|------------------:|
| 2017-12-09 00:00:00.000 | 2018-01-01 00:00:00.000 |        1 | 2018-01-01 00:00:00.000 |       18 |            18 |         28 | Top Dollar |           3 |      35801 |    1017 |            6 |        107 |          0 | 49.99 |            | Med     |              0 |          23 |              0.9  |
| 2017-12-04 00:00:00.000 | 2018-01-01 00:00:00.000 |        1 | 2018-01-01 00:00:00.000 |       25 |            25 |         28 | Top Dollar |           3 |      99501 |    1017 |            6 |        107 |          0 | 49.99 |            | Med     |              0 |          28 |              0.67 |
| 2017-12-17 00:00:00.000 | 2018-01-08 00:00:00.000 |        1 | 2018-01-01 00:00:00.000 |       14 |            14 |         28 | Top Dollar |           3 |       6101 |    1017 |            6 |        107 |          0 | 49.99 |            | Med     |              0 |          22 |              0.95 |
| 2017-12-18 00:00:00.000 | 2018-01-08 00:00:00.000 |        1 | 2018-01-01 00:00:00.000 |        6 |             6 |         28 | Top Dollar |           3 |      11962 |    1017 |            6 |        107 |          0 | 49.99 |            | Med     |              0 |          21 |              1    |
| 2017-12-05 00:00:00.000 | 2018-01-08 00:00:00.000 |        1 | 2018-01-01 00:00:00.000 |       16 |            16 |         28 | Top Dollar |           3 |      20001 |    1017 |            6 |        107 |          0 | 49.99 |            | Med     |              0 |          34 |              0.5  |
| 2017-12-22 00:00:00.000 | 2018-01-08 00:00:00.000 |        1 | 2018-01-01 00:00:00.000 |       19 |            19 |         28 | Top Dollar |           3 |      46201 |    1017 |            6 |        107 |          0 | 49.99 |            | Med     |              0 |          17 |              1    |
| 2017-12-18 00:00:00.000 | 2018-01-08 00:00:00.000 |        1 | 2018-01-01 00:00:00.000 |        5 |             5 |         28 | Top Dollar |           3 |      94957 |    1017 |            6 |        107 |          0 | 49.99 |            | Med     |              0 |          21 |              1    |
| 2017-12-14 00:00:00.000 | 2018-01-08 00:00:00.000 |        1 | 2018-01-01 00:00:00.000 |       25 |            25 |         28 | Top Dollar |           3 |      99501 |    1017 |            6 |        107 |          0 | 49.99 |            | Med     |              0 |          25 |              0.81 |
| 2017-12-30 00:00:00.000 | 2018-01-15 00:00:00.000 |        1 | 2018-01-01 00:00:00.000 |        9 |             9 |         28 | Top Dollar |           3 |       1106 |    1017 |            6 |        107 |          0 | 49.99 |            | Med     |              0 |          16 |              1    |
| 2017-12-29 00:00:00.000 | 2018-01-15 00:00:00.000 |        1 | 2018-01-01 00:00:00.000 |        2 |             2 |         28 | Top Dollar |           3 |      33462 |    1017 |            6 |        107 |          0 | 49.99 |            | Med     |              0 |          17 |              1    |


Table: Seasonality Profiles
| Brand    | Month     |   Seasonality Factor |
|:---------|:----------|---------------------:|
| BuildNow | January   |                 0.02 |
| BuildNow | February  |                 0.04 |
| BuildNow | March     |                 0.05 |
| BuildNow | April     |                 0.05 |
| BuildNow | May       |                 0.05 |
| BuildNow | June      |                 0.03 |
| BuildNow | July      |                 0.04 |
| BuildNow | August    |                 0.06 |
| BuildNow | September |                 0.1  |
| BuildNow | October   |                 0.12 |

 


tables: name: 20Dates
data: 20Dates.csv
name: Annual Brand forecast
data: Annual Brand forecast.csv
name: BOYFCTRACKER
data: BOYFCTRACKER.csv
name: Buyer-Category
data: Buyer-Category.csv
name: Calendar
data: Calendar.csv
name: Cat _URL
data: Cat _URL.csv
name: Customers
data: Customers.csv
name: Dates Alt
data: Dates Alt.csv
name: Dates
data: Dates.csv
name: Forecast
data: Forecast.csv
name: Metric Selection
data: Metric Selection.csv
name: Monthly FCast by Brand
data: Monthly FCast by Brand.csv
name: New Measures
data: New Measures.csv
name: Order-Calendar
data: Order-Calendar.csv
name: Products
data: Products.csv
name: Region
data: Region.csv
name: Sales
data: Sales.csv
name: Seasonality Profiles
data: Seasonality Profiles.csv
label: Sales
Buyer_ID
Buyer-Category
Buyer-ID
Sales
Item_Index
Products
Item_Index
Sales
Store_Index
Customers
Store Index
Sales
Order_Date
Order-Calendar
Date
Forecast
Date
Calendar
Date
Customers
Zip_ID
Region
Zip_ID
Buyer-Category
Category
Cat _URL
Category
Sales
Order_Date
Calendar
Date
Monthly FCast by Brand
Brand
Annual Brand forecast
Brand
Seasonality Profiles
Brand
Annual Brand forecast
Brand
20Dates
MonthName
Seasonality Profiles
Month
output: {
    "joins": [
        {
            "from_table": "Sales",
            "from_column": "Buyer_ID",
            "to_table": "Buyer-Category",
            "to_column": "Buyer-ID"
        },
        {
            "from_table": "Sales",
            "from_column": "Item_Index",
            "to_table": "Products",
            "to_column": "Item_Index"
        },
        {
            "from_table": "Sales",
            "from_column": "Store_Index",
            "to_table": "Customers",
            "to_column": "Store Index"
        },
        {
            "from_table": "Sales",
            "from_column": "Order_Date",
            "to_table": "Order-Calendar",
            "to_column": "Date"
        },
        {
            "from_table": "Forecast",
            "from_column": "Date",
            "to_table": "Calendar",
            "to_column": "Date"
        },
        {
            "from_table": "Customers",
            "from_column": "Zip_ID",
            "to_table": "Region",
            "to_column": "Zip_ID"
        },
        {
            "from_table": "Buyer-Category",
            "from_column": "Category",
            "to_table": "Cat _URL",
            "to_column": "Category"
        },
        {
            "from_table": "Sales",
            "from_column": "Order_Date",
            "to_table": "Calendar",
            "to_column": "Date"
        },
        {
            "from_table": "Monthly FCast by Brand",
            "from_column": "Brand",
            "to_table": "Annual Brand forecast",
            "to_column": "Brand"
        },
        {
            "from_table": "Seasonality Profiles",
            "from_column": "Brand",
            "to_table": "Annual Brand forecast",
            "to_column": "Brand"
        },
        {
            "from_table": "20Dates",
            "from_column": "MonthName",
            "to_table": "Seasonality Profiles",
            "to_column": "Month"
        }
    ]
}