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Transaction Fact Tables Contain Additive Facts
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Lastly, recall that transaction fact tables normally record additive facts. As you learned in 3, most nonadditive measurements, such as ratios, can and should be broken down into fully additive components. This allows the granular data in the fact table to be aggregated to any desired level of detail, after which the ratio or nonadditive fact can be computed. You want the ratio of the sums, not the sum of the ratios. For example, if two products sell at a 10 percent margin, it is incorrect to say the total margin rate is 20 percent. Instead, the components of the margin rate margin dollars and order dollars are stored. When margin rate is needed, they are fetched and aggregated to whatever level of summarization is desired. Their ratio is computed only after any aggregation is performed. Storing fully additive facts provides the most flexible analytic solution.
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Snapshot Fact Tables
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Sometimes, measuring the effect of a series of transactions is as useful as measuring the transactions themselves. These effects are called status measurements. Common examples include account balances and inventory levels. Status can often be discerned by aggregating the transactions that contribute to it. You can figure out how many ballpoint pens are in the stockroom, for example, by adding up all the deliveries of ballpoint pens to the stockroom and deducting all the pens that were removed from the stockroom. This will give you the number of pens remaining, but it is a highly inefficient process. Some status measurements cannot be described as the effect of a series of transactions. Examples include the water level in a reservoir, the air pressure inside a piece of industrial machinery, the oxygen level in the air, and the ambient temperature on a factory floor. These status measurements also describe levels, but it is not practical to describe them as a series of changes. When the measurement of status is important, a transaction fact table is inefficient at best. The solution is an alternative design called a periodic snapshot fact table, or simply snapshot fact table. The snapshot fact table samples the measurement in question at a predetermined interval. This makes it easy to study the measurement in question, without the need to aggregate a long chain of transaction history.
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Part IV
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PART IV
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Fact Table Design
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The Challenge: Studying Status
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A status measurement, such as an account balance, can often be constructed from transaction history. This is an inefficient way to monitor status, however, if the transaction history stretches very far into the past, or if it is necessary to compute the status of numerous things. If status is to be analyzed, it will be necessary to store it somewhere. One might be tempted to do so in the same fact table that records transactions, but this turns out to be a poor solution, and it will not work at all for status measurements that do not have corresponding transactions.
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The Transaction Design Falls Short
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Many status measurements represent the sum total of transactions up to a particular point in time. Your bank account, for example, can be fully described as a series of deposits, checks written, interest payments, fees, and so forth. Each transaction represents a change, or delta, that affects a very important status measurement: your account balance. Imagine if determining your account balance at any point in time required consulting the entire history of transactions, starting with your initial deposit and accumulating them up to the current point in time. Worse, if a bank wanted to look at the total deposits of all customers, rather than just yours, it would have to do this for every current account. That could be quite a lot of transactions. The transaction fact table in Figure 11-1 tracks the activity in bank accounts. Its grain is defined as one row per transaction per account. The day and time dimensions capture the time of the transaction; the account dimension captures the account affected by the transaction, and the degenerate dimension transaction_id identifies the transaction itself. Per the grain statement, a transfer between two accounts will generate two rows, one for each account affected. The type of transaction is captured in a transaction_type dimension, which includes rows for deposits, withdrawals, checks, fees, interest, and so forth. NoTe The design in Figure 11-1 employs some of the advanced dimensional techniques covered in Part III of this book. The branch dimension is present in two roles: one indicates the branch at which the account is managed, and the other represents the branch at which the transaction took place. Since some transactions, such as online transfers, may not occur at a branch, the branch table will contain a special row for not applicable, as described in 6. Similarly, there is a teller dimension which will not always apply. It too will have a special row for not applicable. The account_holder dimension is present in the role of primary account holder. Accounts may have more than one account holder, and, though not shown, this might be dealt with by using an attribute bridge, as described in 9, to link the account dimension to an account_holder outrigger. The account_ facts star provides for rich and varied analysis of transaction activity. It is possible to produce a list of transactions for a specific account, group aggregated transactions by branch, or study transactions of a particular type. All of these options may be quite useful. Missing, however, is an effective way to study account balances, which is a crucial metric. Account balances are used to compute interest payments or levy fees; total deposits at each branch are used to determine the amount of cash kept in the vault, and so forth. While balances can be determined from transaction history, doing so for a single account requires aggregating all transactions starting from its inception. The sample data at the bottom of Figure 11-1 represents a series of transactions stored for a single account during the first two
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