Data source size statistics:

To compute grain size, you should use these estimates about cardinalities of tables and

unique values of some columns.

$$ Product rows: $1,000$

$$ Supplier rows: $100$

$$ Purchase rows: $100,000$ per year

$$ PurchLine rows: $500,000$ per year

$$ Spreadsheet rows: $1,000$ per month; new spreadsheet each month

$$ Unique products in a spreadsheet for one year: $100$

$$ Unique suppliers in a spreadsheet for one year: $20$

Business Needs:

The main purpose of the data warehouse is to track inventory balances over time.

Inventory balances are a type of snapshot. Snapshots are typical in applications in which

balances are involved, such as account balances in financial services, enrollment in courses,

reservations in hospitality and travel, and head count in personnel management. Snapshots

cannot be aggregated over time correctly. Summing quantities and values over time is not

meaningful.

The basic values for inventory tracking are quantity on hand and inventory value.

Inventory valuation can be complex as many accounting methods exist to value inventory. For

this case, the purchase price or unit cost of the inventory can be used for valuation. The data

warehouse should support detailed tracking of inventory to the individual product, purchased by

date, and supplier.

Here are typical computations for analyzing and tracking inventory balances using the

quantity on hand and inventory value:

$$ The average quantities and stock values in each time period

$$ The opening and closing balances for each time period

$$ The change in inventory levels between consecutive periods and parallel periods

$$ The minimum and maximum inventory levels in a time period

$$ The relative contribution of the stocked item to the overall stock value

$**$PLEASE ANSWER ALL OF THE QUESTIONS$**$

Problems:

$1.$ You should identify dimensions, map dimensions to data sources, and specify dimension

hierarchies. For each dimension, you should identify its data sources and attributes in each

data source. For hierarchical dimensions, you should indicate the levels from broad to

narrow.

$2.$ You should specify measures, related data sources, and measure aggregation properties.

$3.$ Identify the grain in your dimensional design using the business needs as a guideline. You

should then indicate relative storage requirements for the grain using the statistics for the data

sources. Using the cardinality estimates provided, you should determine either the fact table

size or sparsity and then compute the unknown grain size variable. For example, you should

compute sparsity if the fact table size is given.

$4.$ Extend your analysis to design a star schema $($or variation$)$ to support inventory analysis. For

each table, you should define the table name, primary key, and columns. You do not need to

write complete CREATE TABLE statements.

$5.$ Identify summarizability problems in your star schema and indicate preferred resolutions of

the summarizability problems. For incomplete dimension$-$fact relationships, you should also

indicate if columns in a dimension table allow null values.

$6.$ You should populate your data warehouse tables based on the data in the sample tables and

spreadsheet. You do not need to write SQL INSERT statements or insert the data into your

tables. You can just show table listings in your solution. You should indicate mappings from

data sources into tables. For example, a mapping may involve generating new primary key

values for a data warehouse table or using a default value for a missing value.