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The snapshot model in Figure 11-2 provides a different, but equally important, view of this process, allowing analysis of balances across a slightly smaller set of dimensions Since the snapshot samples the day-end balance, dimensions associated with individual transactions are not included These include the transaction type, teller, branch where the transaction took place, and degenerate dimension identifying a specific transaction When a design will include both a transaction fact table and a periodic snapshot, the snapshot can and should be designed to use the transaction fact table as its data source This eliminates what would otherwise be duplicative ETL processing of the source data, both for the transactions as well as for the associated dimensions It also ensures that dimensional data will be identified and loaded consistently TIP Transaction and snapshot models tend to complement each other nicely.

Figure 8-21.

If both are to be built, design the snapshot to use the transaction star as its data source Notice that it is not usually possible to do the reverse construct transactions from snapshots If you have any doubts about this assertion, refer back to Figure 11-3 You will see, for example, that there is a group of days during which a particular account has the same balance: February 3 6 It is not possible to tell from the snapshot whether there was.

SQL allows you to define a view like this one for large orders: SELECT * FROM PRODUCTS WHERE AMOUNT > 10000 and then use the view as if it were a "real" table in the FROM clause of a query to find out which products, in what quantities, were ordered in these large orders: SELECT MFR, PRODUCT, SUM(QTY) FROM BIGORDERS GROUP BY MFR, PRODUCT Conceptually, SQL should let you substitute the view definition right into the query, like this: SELECT MFR, PRODUCT, SUM(QTY) FROM (SELECT * FROM ORDERS WHERE AMOUNT > 10000) GROUP BY MFR, PRODUCT But the SQL1 standard doesn't allow a subquery in this position in the WHERE clause Yet clearly the DBMS should be able to determine the meaning of this query, since it.

8:

Students of dimensional design often ask if the snapshot model can be considered an aggregate of the transaction model. The answer is both yes and no. Clearly, a snapshot can be created using the transactional data as its source. For some periods, it may be necessary to factor in several transactions. In this sense, the snapshot is an aggregate. The term aggregate, however, is reserved for a particular form of summarization one that contains exactly the same facts as the original star. This concept will be explored in full detail in 15. In the case of a snapshot, the facts are decidedly different from the transaction star. In this sense, the snapshot is not a pure aggregate; it more closely fits the definition of a derived table, as described in 14. From a purely pragmatic perspective, then, you may call a snapshot whatever you like. It certainly aggregates data, and you can think of it as a summary or aggregate if you like. If you do, be sure not to confuse its capabilities with those of the aggregate stars described in 15, which answer exactly the same questions as their source stars.

Instance Distribution Logreader Merge Snapshot [list of publications]

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no activity between these snapshots, as is the case for February 4 and February 5, or whether there were offsetting transactions that netted out to a change in the balance of zero, as was the case on February 6. As previously mentioned, cases exist where there is no transaction data associated with a periodic snapshot. Common examples include things like temperature levels, water levels, and other measurements that sample conditions. In other cases, transactions may contribute to the balance or level, but may be considered too numerous to maintain in any repository. In these cases, a snapshot model may be the only representation of a process and will be sourced directly from an operational system. The operational system itself may sample the status itself, rather than record individual transactions.

Notes Displays performance information related to the various Agents that perform replication tasks. Useful for measuring the latency between various Subscribers, and can reflect current network conditions. Useful for measuring the latency between various Subscribers, and can reflect current network conditions and resource usage of the current server. Measures the amount of activity being recorded in merge replication; useful for determining data modification and usage statistics. Indicates how many replication conflicts have occurred at Subscribers. A high number indicates that many Subscribers are updating the same information and that important data might be overwritten in conflict resolution. Indicates the number of commands executed per second in snapshot replication. Useful for determining how many changes are being replicated per unit time and evaluating the performance of the overall synchronization.