Using the Sales History sample data

A Sales History data set is supplied as a sample data set for an Endeca data domain.

The Sales History data set is used by a sample company that does a high volume of business. It therefore runs business statistics reports to aid in decision making. Many of these reports are time-based and nonvolatile (that is, they analyze past data trends). The statistics in the SH data set include annual, quarterly, monthly, and weekly sales figures by product. The company also runs reports on distribution channels through which its sales are delivered. When the company runs special promotions on its products, it analyzes the impact of the promotions on sales. It also analyzes sales by geographical area.

The Sales History data set is ready for use out of the box because the files have already been indexed by the Endeca Server. This eliminates the need to load source records into the Endeca Server. Note that the Sales History data set has one collection named Base.

Before you can use the Endeca data domain configured for the Sales History data set, place the Sales History indexed files into the offline directory of the Endeca Server and import them into a newly-created Endeca data domain.

To unpack and use the sample data files:

Unpack the sample data ZIP file to a directory of your choice.

A directory named sh_indexes is created.
Make sure that both WebLogic Server and the Endeca Server application are up and running.
Copy the sh_indexes directory to the Endeca Server's offline directory.

The location of the offline directory is set by the endeca-offline-dir parameter in the Endeca Server configuration file (named EndecaServer.properties, and located by default in the $DOMAIN_HOME/config directory).
Use the endeca-cmd import-dd command to create and enable an Endeca data domain, using the sh data files.
You must specify a data domain that is not already in use. For example:
```
endeca-cmd import-dd sh_sample --offline-name sh
```

The Endeca data domain named "sh_sample" is ready for use.

The Sales History data set has the following attributes:

Attribute name and data type	Attribute name and data type	Attribute name and data type
`AFFINITY_CARD` [boolean]	`CUST_MAIN_PHONE_NUMBER` [string]	`PROD_EFF_FROM` [dateTime]
`AMOUNT_SOLD` [double]	`CUST_MARITAL_STATUS` [string]	`PROD_EFF_TO` [dateTime]
`CALENDAR_MONTH_DESC` [string]	`CUST_POSTAL_CODE` [string]	`PROD_FK` [long]
`CALENDAR_MONTH_ID` [long]	`CUST_SRC_ID` [long]	`PROD_ID` [long]
`CALENDAR_MONTH_NAME` [string]	`CUST_STATE_PROVINCE` [string]	`PROD_LIST_PRICE` [double]
`CALENDAR_MONTH_NUMBER` [long]	`CUST_STATE_PROVINCE_ID` [long]	`PROD_MIN_PRICE` [double]
`CALENDAR_QUARTER_DESC` [string]	`CUST_STREET_ADDRESS` [string]	`PROD_NAME` [string]
`CALENDAR_QUARTER_ID` [long]	`CUST_TOTAL` [string]	`PROD_PACK_SIZE` [string]
`CALENDAR_QUARTER_NUMBER` [long]	`CUST_TOTAL_ID` [long]	`PROD_SRC_ID` [long]
`CALENDAR_WEEK_NUMBER` [long]	`CUST_VALID` [string]	`PROD_STATUS` [string]
`CALENDAR_YEAR` [long]	`CUST_YEAR_OF_BIRTH` [long]	`PROD_SUBCATEGORY` [string]
`CALENDAR_YEAR_ID` [long]	`DAY_NAME` [string]	`PROD_SUBCATEGORY_DESC` [string]
`CalendarDate` [string]	`DAY_NUMBER_IN_MONTH` [long]	`PROD_SUBCATEGORY_ID` [long]
`Channel` [string]	`DAY_NUMBER_IN_WEEK` [long]	`PROD_TOTAL` [string]
`CHANNEL_CLASS` [string]	`DAYS_IN_CAL_MONTH` [long]	`PROD_TOTAL_ID` [long]
`CHANNEL_CLASS_ID` [long]	`DAYS_IN_CAL_QUARTER` [long]	`PROD_UNIT_OF_MEASURE` [string]
`CHANNEL_DESC` [string]	`DAYS_IN_CAL_YEAR` [long]	`PROD_VALID` [string]
`CHANNEL_FK` [long]	`DAYS_IN_FIS_MONTH` [long]	`PROD_WEIGHT_CLASS` [long]
`CHANNEL_ID` [long]	`DAYS_IN_FIS_QUARTER` [long]	`ProductCategory` [string]
`CHANNEL_TOTAL` [string]	`DAYS_IN_FIS_YEAR` [long]	`PROMO_BEGIN_DATE` [dateTime]
`CHANNEL_TOTAL_ID`	`EDUCATION` [string]	`PROMO_CATEGORY` [string]
`COMMENTS` [string]	`END_OF_CAL_MONTH` [dateTime]	`PROMO_CATEGORY_ID` [long]
`COUNTRY_FK` [long]	`END_OF_CAL_QUARTER` [dateTime]	`PROMO_COST` [double]
`COUNTRY_ID` [long]	`END_OF_CAL_YEAR` [dateTime]	`PROMO_END_DATE` [dateTime]
`COUNTRY_ISO_CODE` [string]	`END_OF_FIS_MONTH` [dateTime]	`PROMO_FK` [long]
`COUNTRY_NAME` [string]	`END_OF_FIS_QUARTER` [dateTime]	`PROMO_ID` [long]
`COUNTRY_NAME_HIST` [string]	`END_OF_FIS_YEAR` [dateTime]	`PROMO_NAME` [string]
`COUNTRY_REGION` [string]	`FISCAL_MONTH_DESC` [string]	`PROMO_SUBCATEGORY` [string]
`COUNTRY_REGION_ID` [long]	`FISCAL_MONTH_ID` [long]	`PROMO_SUBCATEGORY_ID` [long]
`COUNTRY_SUBREGION` [string]	`FISCAL_MONTH_NAME` [string]	`PROMO_TOTAL` [string]
`COUNTRY_SUBREGION_ID` [long]	`FISCAL_MONTH_NUMBER` [long]	`PROMO_TOTAL_ID` [long]
`COUNTRY_TOTAL` [string]	`FISCAL_QUARTER_DESC` [string]	`Promotion` [string]
`COUNTRY_TOTAL_ID` [long]	`FISCAL_QUARTER_ID` [long]	`PurchaseHistory` [string]
`CUST_CITY` [string]	`FISCAL_QUARTER_NUMBER` [long]	`QUANTITY_SOLD` [long]
`CUST_CITY_ID` [long]	`FISCAL_WEEK_NUMBER` [long]	`SUPPLIER_FK` [long]
`CUST_CREDIT_LIMIT` [long]	`FISCAL_YEAR` [long]	`TIME_FK` [dateTime]
`CUST_EFF_FROM` [dateTime]	`FISCAL_YEAR_ID` [long]	`TIME_ID` [dateTime]
`CUST_EFF_TO` [dateTime]	`FiscalDate` [string]	`TRANSACTION_ID` [long]
`CUST_EMAIL` [string]	`Geography` [string]	`UNIT_COST` [double]
`CUST_FIRST_NAME` [string]	`HOUSEHOLD_SIZE` [string]	`UNIT_PRICE` [double]
`CUST_FK` [long]	`OCCUPATION` [string]	`WEEK_ENDING_DAY` [dateTime]
`CUST_GENDER` [string]	`PROD_CATEGORY` [string]	`WEEK_ENDING_DAY_ID` [long]
`CUST_ID` [long]	`PROD_CATEGORY_DESC` [string]	`YRS_RESIDENCE` [long]
`CUST_INCOME_LEVEL` [string]	`PROD_CATEGORY_ID` [long]
`CUST_LAST_NAME` [string]	`PROD_DESC` [string]

Of these attributes, the following are managed attributes:

CalendarDate
FiscalDate
Geography
ProductCategory
Promotion