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Remote database access is very convenient for small remote queries and occasional remote database access. If an application requires heavy and frequent access to a remote database, however, the communications overhead of remote database access can become large. Once remote access grows beyond a certain point, it is often more efficient to maintain a local copy of the remote data in the local database. Many of the DBMS vendors provide tools to simplify the process of data extraction and distribution. In its simplest form, the process extracts the contents of a table in a master database, sends it across a network to another system, and loads it into a corresponding replica table in a slave database, as shown in Figure 23-3. In practice, the extract is performed periodically and during off-peak times of database activity. This approach is very appropriate when the data in the replicated table changes slowly, or when changes to the table naturally occur in a batch. For example, suppose some tables of the sample database, located on a remote central computer system, are to be replicated in a local database. The contents of the OFFICES table hardly ever change. It would be an excellent candidate for replication onto distribution center or sales force automation databases. Once the initial (local) replica tables are set up and populated, they might need to be updated only once per month, or when a new sales office is opened. The PRODUCTS table is also a good candidate for replication. Product price changes occur more frequently than office changes, but in most companies, they happen in batches, perhaps once a week or once a day. With this natural processing cycle, it would be very
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Tables
Periodic replication
Tables
Slave database
Read-only
Read-only
FIGURE 23-3
A basic master/slave replicated architecture
effective to extract a table of product price data just after each batch of updates, and to send it to the distribution center databases and the sales force automation central database. The price data in these databases does not need to be tightly linked to the mainframe database to ensure that it is fresh. A weekly or daily extract/update cycle will make the data just as current, with a substantially smaller processing workload. It s possible to implement this type of replicated-table strategy without any support from the DBMS. You could write an application program that uses SQL on the mainframe to extract the product pricing data into a file. A file transfer program could transmit the file to the distribution centers, where another application program could read its contents and generate the appropriate DROP TABLE, CREATE TABLE, and INSERT statements to populate the replicated table. The first step toward automating this strategy was the development of high-speed data extract and data loading programs. These utility programs, offered by the DBMS vendors, typically use proprietary, lower-level database access techniques to extract the data and load the data much more rapidly than is possible through SQL SELECT and INSERT statements. More recently, software companies have targeted this area as an opportunity for stand-alone software packages, independent of the DBMS vendors. This category of software, called extract, transform, and load (ETL) software, focuses on linking disparate database systems and file formats. ETL tools typically offer a graphical user interface for specifying the data extraction, an array of tools for reformatting data between the source and destination systems, a messaging capability for transmitting the data, perhaps a store-and-forward capability for staging extracted data before and after transmission, and utilities for managing and monitoring the overall process. Two additional categories of integration software are enterprise application integration (EAI) and enterprise information integration (EII). While there is some overlap with ETL,
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