The extraction method you should choose is highly dependent on the source system and also from the business needs in the target data warehouse environment. Very often, there is no possibility to add additional logic to the source systems to enhance an incremental extraction of data due to the performance or the increased workload of these systems. Sometimes even the customer is not allowed to add anything to an out-of-the-box application system.
The estimated amount of the data to be extracted and the stage in the ETL process (initial load or maintenance of data) may also impact the decision of how to extract, from a logical and a physical perspective. Basically, you have to decide how to extract data logically and physically.
Logical Extraction Methods
There are two types of logical extraction:
The data is extracted completely from the source system. Because this extraction reflects all the data currently available on the source system, there's no need to keep track of changes to the data source since the last successful extraction. The source data will be provided as-is and no additional logical information (for example, timestamps) is necessary on the source site. An example for a full extraction may be an export file of a distinct table or a remote SQL statement scanning the complete source table.
At a specific point in time, only the data that has changed since a well-defined event back in history will be extracted. This event may be the last time of extraction or a more complex business event like the last booking day of a fiscal period.To identify this delta change there must be a possibility to identify all the changed information since this specific time event. This information can be either provided by the sourcedata itself such as an application column, reflecting the last-changed timestamp or a change table where an appropriate additional mechanism keeps track of the changes besides the originating transactions. In most cases, using the latter method means adding extraction logic to the source system.
Many data warehouses do not use any change-capture techniques as part of the extraction process. Instead, entire tables from the source systems are extracted to the data warehouse or staging area, and these tables are compared with a previous extract from the source system to identify the changed data. This approach may not Introduction to have significant impact on the source systems, but it clearly can place a considerable burden on the data warehouse processes, particularly if the data volumes are large.Oracle's Change Data Capture mechanism can extract and maintain such delta information.
Physical Extraction Methods
Depending on the chosen logical extraction method and the capabilities and restrictions on the source side, the extracted data can be physically extracted by two mechanisms. The data can either be extracted online from the source system or from an offline structure. Such an offline structure might already exist or it might be generated by an extraction routine.
There are the following methods of physical extraction:
The data is extracted directly from the source system itself. The extraction process can connect directly to the source system to access the source tables themselves or to an intermediate system that stores the data in a preconfigured manner (for example, snapshot logs or change tables). Note that the intermediate system is not necessarily physically different from the source system.
With online extractions, you need to consider whether the distributed transactions are using original source objects or prepared source objects.
The data is not extracted directly from the source system but is staged explicitly outside the original source system. The data already has an existing structure (for example, redo logs, archive logs or transportable tablespaces) or was created by an extraction routine.
You should consider the following structures:
Data in a defined, generic format. Additional information about the source object is necessary for further processing.
Oracle-specific format. Information about the containing objects may or may not be included, depending on the chosen utility.
Information is in a special, additional dump file.
A powerful way to extract and move large volumes of data between Oracle databases. Oracle Corporation recommends that you use transportable tablespaces whenever possible, because they can provide considerable advantages in performance and manageability over other extraction techniques.
Change Data Capture
An important consideration for extraction is incremental extraction, also called Change Data Capture. If a data warehouse extracts data from an operational system on a nightly basis, then the data warehouse requires only the data that has changed since the last extraction (that is, the data that has been modified in the past 24 hours). Change Data Capture is also the key-enabling technology for providing nearreal-time, or on-time, data warehousing.
When it is possible to efficiently identify and extract only the most recently changed data, the extraction process (as well as all downstream operations in the ETL process) can be much more efficient, because it must extract a much smaller volume of data. Unfortunately, for many source systems, identifying the recently modified data may be difficult or intrusive to the operation of the system. Change Data Capture is typically the most challenging technical issue in data extraction.
Because change data capture is often desirable as part of the extraction process and it might not be possible to use the Change Data Capture mechanism, this section describes several techniques for implementing a self-developed change capture on Oracle Database source systems:
These techniques are based upon the characteristics of the source systems, or may require modifications to the source systems. Thus, each of these techniques must be carefully evaluated by the owners of the source system prior to implementation. Each of these techniques can work in conjunction with the data extraction technique discussed previously. For example, timestamps can be used whether the data is being unloaded to a file or accessed through a distributed query.
The tables in some operational systems have timestamp columns. The timestamp specifies the time and date that a given row was last modified. If the tables in an operational system have columns containing timestamps, then the latest data can easily be identified using the timestamp columns. For example, the following query might be useful for extracting today's data from an orders table:
If the timestamp information is not available in an operational source system, you will not always be able to modify the system to include timestamps. Such modification would require, first, modifying the operational system's tables to include a new timestamp column and then creating a trigger to update thetimestamp column following every operation that modifies a given row.
Some source systems might use range partitioning, such that the source tables are partitioned along a date key, which allows for easy identification of new data. For example, if you are extracting from an orders table, and the orders table is partitioned by week, then it is easy to identify the current week's data.
Triggers can be created in operational systems to keep track of recently updated records. They can then be used in conjunction with timestamp columns to identify the exact time and date when a given row was last modified. You do this by creating a trigger on each source table that requires change data capture. Following each DML statement that is executed on the source table, this trigger updates the timestamp column with the current time. Thus, the timestamp column provides the exact time and date when a given row was last modified.
A similar internalized trigger-based technique is used for Oracle materialized view logs. These logs are used by materialized views to identify changed data, and these logs are accessible to end users. However, the format of the materialized view logs is not documented and might change over time.
If you want to use a trigger-based mechanism, use synchronous change data capture. It is recommended that you use synchronous Change Data Capture for trigger based change capture, because CDC provides an externalized interface for accessing the change information and provides a framework for maintaining the distribution of this information to various clients.
Materialized view logs rely on triggers, but they provide an advantage in that the creation and maintenance of this change-data system is largely managed by the database.
However, Oracle Corporation recommends the usage of synchronous Change Data Capture for trigger-based change capture, since CDC provides an externalized interface for accessing the change information and provides a framework for maintaining the distribution of this information to various clients Trigger-based techniques might affect performance on the source systems, and this impact should be carefully considered prior to implementation on a production source system.
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Data Warehousing Tutorial
Data Warehousing Concepts
Physical Design In Data Warehouses
Hardware And I/o Considerations In Data Warehouses
Parallelism And Partitioning In Data Warehouses
Basic Materialized Views
Advanced Materialized Views
Overview Of Extraction, Transformation, And Loading
Extraction In Data Warehouses
Transportation In Data Warehouses
Loading And Transformation
Maintaining The Data Warehouse
Change Data Capture
Schema Modeling Techniques
Sql For Aggregation In Data Warehouses
Sql For Analysis And Reporting
Sql For Modeling
Olap And Data Mining
Using Parallel Execution
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