Data Warehousing Extraction Examples - Data Warehousing

You can extract data in two ways:

  • Extraction Using Data Files
  • Extraction Through Distributed Operations

Extraction Using Data Files
Most database systems provide mechanisms for exporting or unloading data from the internal database format into flat files. Extracts from mainframe systems often use COBOL programs, but many databases, as well as third-party software vendors, provide export or unload utilities.

Data extraction does not necessarily mean that entire database structures are unloaded in flat files. In many cases, it may be appropriate to unload entire database tables or objects. In other cases, it may be more appropriate to unload only a subset of a given table such as the changes on the source system since the last extraction or the results of joining multiple tables together. Different extraction techniques vary in their capabilities to support these two scenarios.

When the source system is an Oracle database, several alternatives are available for extracting data into files:

  • Extracting into Flat Files Using SQL*Plus
  • Extracting into Flat Files Using OCI or Pro*C Programs
  • Exporting into Export Files Using the Export Utility
  • Extracting into Export Files Using External Tables

Extracting into Flat Files Using SQL*Plus
The most basic technique for extracting data is to execute a SQL query in SQL*Plus and direct the output of the query to a file. For example, to extract a flat file, country_city.log, with the pipe sign as delimiter between column values, containing a list of the cities in the US in the tables countries and customers, thefollowing SQL script could be run:

SPOOL off

The exact format of the output file can be specified using SQL*Plus system variables.

This extraction technique offers the advantage of storing the result in a customized format. Note that using the external table data pump unload facility, you can also extract the result of an arbitrary SQL operation. The example previously extracts the results of a join.

This extraction technique can be parallelized by initiating multiple, concurrent SQL*Plus sessions, each session running a separate query representing a different portion of the data to be extracted. For example, suppose that you wish to extract data from an orders table, and that the orders table has been range partitioned by month, with partitions orders_jan1998, orders_feb1998, and so on. To extract a single year of data from the orders table, you could initiate 12 concurrent SQL*Plus sessions, each extracting a single partition. The SQL script for one such session could be:

These 12 SQL*Plus processes would concurrently spool data to 12 separate files. You can then concatenate them if necessary (using operating system utilities) following the extraction. If you are planning to use SQL*Loader for loading into the target, these 12 files can be used as is for a parallel load with 12 SQL*Loader sessions.

Even if the orders table is not partitioned, it is still possible to parallelize the extraction either based on logical or physical criteria. The logical method is based on logical ranges of column values, for example:

The physical method is based on a range of values. By viewing the data dictionary, it is possible to identify the Oracle Database data blocks that make up the orders table. Using this information, you could then derive a set of rowid-range queries for extracting data from the orders table:

Parallelizing the extraction of complex SQL queries is sometimes possible, although the process of breaking a single complex query into multiple components can be challenging. In particular, the coordination of independent processes to guarantee a globally consistent view can be difficult. Unlike the SQL*Plus approach, using the new external table data pump unload functionality provides transparent parallel capabilities.

Note that all parallel techniques can use considerably more CPU and I/O resources on the source system, and the impact on the source system should be evaluated before parallelizing any extraction technique.

Extracting into Flat Files Using OCI or Pro*C Programs
OCI programs (or other programs using Oracle call interfaces, such as Pro*C programs), can also be used to extract data. These techniques typically provide improved performance over the SQL*Plus approach, although they also require additional programming. Like the SQL*Plus approach, an OCI program can extract the results of any SQL query. Furthermore, the parallelization techniques described for the SQL*Plus approach can be readily applied to OCI programs as well.

When using OCI or SQL*Plus for extraction, you need additional information besides the data itself. At minimum, you need information about the extracted columns. It is also helpful to know the extraction format, which might be the separator between distinct columns.

Exporting into Export Files Using the Export Utility
The Export utility allows tables (including data) to be exported into Oracle Database export files. Unlike the SQL*Plus and OCI approaches, which describe the extraction of the results of a SQL statement, Export provides a mechanism for extracting database objects. Thus, Export differs from the previous approaches in several important ways:

  • The export files contain metadata as well as data. An export file contains not only the raw data of a table, but also information on how to re-create the table, potentially including any indexes, constraints, grants, and other attributes associated with that table.
  • A single export file may contain a subset of a single object, many database objects, or even an entire schema.
  • Export cannot be directly used to export the results of a complex SQL query. Export can be used only to extract subsets of distinct database objects.
  • The output of the Export utility must be processed using the Import utility.

Oracle provides the original Export and Import utilities for backward compatibility and the data pump export/import infrastructure for high-performant, scalable and parallel extraction.

Extracting into Export Files Using External Tables
In addition to the Export Utility, you can use external tables to extract the results from any SELECT operation.The data is stored in the platform independent, Oracle-internal data pump format and can be processed as regular external table on the target system. The following example extracts the result of a join operation in parallel into the four specified files. The only allowed external table type for extracting data is the Oracle-internal format ORACLE_DATAPUMP.

The total number of extraction files specified limits the maximum degree of parallelism for the write operation. Note that the parallelizing of the extraction does not automatically parallelize the SELECT portion of the statement.

Unlike using any kind of export/import, the metadata for the external table is not part of the created files when using the external table data pump unload. To extract the appropriate metadata for the external table, use the DBMS_METADATA package, as illustrated in the following statement:

SET LONG 2000

Extraction Through Distributed Operations
Using distributed-query technology, one Oracle database can directly query tables located in various different source systems, such as another Oracle database or a legacy system connected with the Oracle gateway technology. Specifically, a data warehouse or staging database can directly access tables and data located in a connected source system. Gateways are another form of distributed-query technology. Gateways allow an Oracle database (such as a data warehouse) to access database tables stored in remote, non-Oracle databases. This is the simplest method for moving data between two Oracle databases because it combines the extraction and transformation into a single step, and requires minimal programming. However, this is not always feasible.

Suppose that you wanted to extract a list of employee names with department names from a source database and store this data into the data warehouse. Using an Oracle Net connection and distributed-query technology, this can be achieved using a single SQL statement:

This statement creates a local table in a data mart, country_city, and populates it with data from the countries and customers tables on the source system.

This technique is ideal for moving small volumes of data. However, the data is transported from the source system to the data warehouse through a single Oracle Net connection. Thus, the scalability of this technique is limited. For larger dataData Warehousing volumes, file-based data extraction and transportation techniques are often more scalable and thus more appropriate.


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