1 / 41

Extraction, Transformation, and Loading (ETL)

Extraction, Transformation, and Loading (ETL). Extraction and Transportation. Objectives. After completing this lesson, you should be able to do the following: Describe the core ETL framework inside the database and its integration advantage Explain data warehousing extraction methods

cheri
Download Presentation

Extraction, Transformation, and Loading (ETL)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Extraction, Transformation,and Loading (ETL) Extraction and Transportation

  2. Objectives • After completing this lesson, you should be able to do the following: • Describe the core ETL framework inside the database and its integration advantage • Explain data warehousing extraction methods • Identify transportation methods: • Flat file • Distributes operations • Transportable tablespaces • Describe transformation flow

  3. Overview • Lesson 4: Extraction/Transportation • Lesson 5: Loading • Lesson 6: Transformation

  4. What Is ETL? • ETL is an acronym for Extraction, Transformation, and Loading. • The following happen during the ETL process: • The desired data is identified and extracted from many different sources. • Some transformations may take place during this extraction process. • After extraction, the data must be transported to a target system or an intermediate system for further processing. • Depending on the method of transportation, some transformations can be done simultaneously. • ETL refers to a broad process.

  5. Extraction Methods • Extraction can be thought of in two parts: • Extraction • Transportation • There are two extraction methods: • Logical • Physical • Your logical choice influences the way the data is physically extracted. • Some criteria for choosing a combination: • Business needs • Location of the source and target systems • Availability of the source system • Time required to extract data

  6. Logical Extraction Methods • There are two kinds of logical extraction: • Full extraction • All data is pulled • Less information to track • More time required to pull the data • Incremental extraction • A subset of data is pulled • Must track what data needs to be pulled • Less time required to pull the data

  7. Physical Extraction Methods • There are two types of physical extraction. • Online extraction: • Pulls data from the source system • Offline extraction: • Pulls data from a staging area • Staging areas include flat files, dump files, and transportable tablespaces.

  8. Offline Extraction • Staging areas: • Flat files • Requires data in a predefined, generic format • Dump files • Must be in an Oracle-specific format • Redo and archive logs • Data located in special dump files • Transportable tablespaces • Powerful, fast method for moving large volumes of data

  9. Implementing Methods of Extraction • Extracting to a file: • Spooling from SQL*Plus • Using OCI or Pro*C to dump to a file • Using Data Pump to export to an Oracle dump file • Using external tables • Extracting through distributed operations

  10. Incremental Extraction Using CDC • CDC can capture and publish committed change data in either of the following modes: • Synchronous • Triggers on the source database allow change data to be captured immediately. • Change data is captured as part of the transaction modifying the source table. • Asynchronous • Change data is captured after a SQL statement performing DML is committed using the redo logs. • Asynchronous Change Data Capture is built on Oracle Streams.

  11. Publish and Subscribe Model • The publisher performs the following tasks: • Identifies source tables from which the data warehouse is interested in capturing change data • Uses the DBMS_CDC_PUBLISH package to: • Set up the capture of data from the source tables • Determine and advance the change sets • Publish the change data • Allows controlled access to subscribers using the SQL GRANTand REVOKEstatements

  12. Publish and Subscribe Model • The subscriber uses the DBMS_CDC_SUBSCRIBE package to: • Subscribe to source tables • Extend the window and create change view • Prepare the subscriber views • View data stored in change tables • Purge the subscriber view • Remove the subscriber views

  13. Synchronous CDC Source database SYNC_SOURCE Change source Source database transactions Change set Trigger execution Change tables Source tables Subscriberviews

  14. Asynchronous CDC • Asynchronous CDC: • Captures change data from redo log files after changes have been committed to the source database • Modes are dependent on the level of supplemental logging used on the source database • Uses Oracle Streams to capture change data from redo log files • Has three source modes: • Asynchronous AutoLog mode • Asynchronous HotLog mode • Asynchronous Distributed HotLog mode

  15. Subscriberviews Asynchronous AutoLog Mode Staging database Source database Distributed AutoLog change set Source database transactions Change set LOG_ARCHIVE_DEST_2 RFS Change tables Source tables LGWR Online redo logs Streamscapture Distributed AutoLog change source Standby redo logs

  16. Asynchronous HotLog Configuration Source database HOTLOG_SOURCE Change Source Source database transactions Change set LGWR Change tables Streams local capture Source tables Online redo logs Subscriberviews

  17. Asynchronous Distributed HotLog Mode Staging database Source database Distributed HotLog change set Source database transactions DBlink Change set Source tables LGWR Streams propagation Change tables Online redo logs Distributed HotLog change source Subscriberviews DBlink

  18. Preparing to Publish Change Data 1. Gather requirements from the subscribers. 2. Determine which source database contains the relevant source tables. 3. Choose the capture mode: • Synchronous • Asynchronous HotLog • Asynchronous Distributed HotLog • Asynchronous AutoLog • Ensure that the source and staging databases have appropriate database initialization parameters set. • Set up database links between the source database and the staging database.

  19. Creating a Publisher User • The staging database publisher must be granted the following privileges and roles: • EXECUTE_CATALOG_ROLE privilege • SELECT_CATALOG_ROLE privilege • CREATE TABLE and CREATE SESSION privileges • EXECUTE on the DBMS_CDC_PUBLISH package • Create a default tablespace for the publisher.

  20. Synchronous Publishing 1. Create a change set. 2. Create a change table. 3. Grant access to subscribers. BEGIN DBMS_CDC_PUBLISH.CREATE_CHANGE_SET( change_set_name => 'CHICAGO_DAILY', description => 'Change set for sales history info', change_source_name => 'SYNC_SOURCE'); END; GRANT SELECT ON cdcpub.products_ct TO subscriber1;

  21. Asynchronous Distributed HotLog Publishing • Prepare the source and staging databases: 1. Configure Oracle Net so that the source database can communicate with the staging database. 2. Set initialization parameters on the source database. 3. Set initialization parameters on the staging database. compatible = 10.2.0 global_names = true job_queue_processes = <current value> + 2 open_links = 4 parallel_max_servers = <current value> + 3 processes = <current value> + 4 sessions = <current value> + 1 streams_pool_size = <current value> + 20 MB undo_retention = 3600

  22. Asynchronous Distributed HotLog Publishing • Prepare the staging database: • Set the database initialization parameters on the staging database. compatible = 10.2.0 global_names = true java_pool_size = 50000000 open_links = 4 job_queue_processes = 2 parallel_max_servers = <current_value> + 2 processes = <current_value> + 3 sessions = <current value> + 1 streams_pool_size = <current_value> + 11 MB undo_retention = 3600

  23. Asynchronous Distributed HotLog Publishing • Alter the source database: 1. Place the database into FORCE LOGGING logging mode to protect against unlogged direct writes. 2. Enable supplemental logging. 3. Create an unconditional log group on all columns to be captured in the source table. ALTER DATABASE FORCE LOGGING; ALTER DATABASE ADD SUPPLEMENTAL LOG DATA; ALTER TABLE SH.PRODUCTS ADD SUPPLEMENTAL LOG GROUP log_group_products (PROD_ID, PROD_NAME, PROD_LIST_PRICE) ALWAYS;

  24. Asynchronous Distributed HotLog Publishing • Publisher privileges on source and staging databases: 1. Create and grant privileges to the source database publisher. 2. Create and grant privileges to the staging database publisher. CREATE USER source_cdcpub IDENTIFIED BY source_cdcpub QUOTA UNLIMITED ON SYSTEM QUOTA UNLIMITED ON SYSAUX; GRANT CREATE SESSION TO source_cdcpub; GRANT DBA TO source_cdcpub; GRANT CREATE DATABASE LINK TO source_cdcpub; GRANT EXECUTE on DBMS_CDC_PUBLISH TO source_cdcpub; GRANT EXECUTE_CATALOG_ROLE TO source_cdcpub; GRANT SELECT_CATALOG_ROLE TO source_cdcpub; EXECUTE DBMS_STREAMS_AUTH.GRANT_ADMIN_PRIVILEGE( GRANTEE=> 'source_cdcpub');

  25. Asynchronous Distributed HotLog Publishing • Create source and staging database links: 1. Create the source database link. 2. Create the staging database link. CREATE DATABASE LINK staging_db CONNECT TO staging_cdcpub IDENTIFIED BY staging_cdcpub USING 'staging_db'; CREATE DATABASE LINK source_db CONNECT TO source_cdcpub IDENTIFIED BY source_cdcpub USING 'source_db';

  26. Asynchronous Distributed HotLog Publishing • Create change sources and change sets: 1. Create the change sources. 2. Create the change sets. BEGIN DBMS_CDC_PUBLISH.CREATE_HOTLOG_CHANGE_SOURCE( change_source_name => 'CHICAGO', description => 'test source', source_database => 'source_db'); END; DBMS_CDC_PUBLISH.CREATE_CHANGE_SET( change_set_name => 'CHICAGO_DAILY', description => 'change set for product info', change_source_name => 'CHICAGO', stop_on_ddl => 'y'); END;

  27. Asynchronous Distributed HotLog Publishing • Create the change tables on the staging database: BEGIN DBMS_CDC_PUBLISH.CREATE_CHANGE_TABLE( owner => 'staging_cdcpub', change_table_name => 'products_ct', change_set_name => 'CHICAGO_DAILY', source_schema => 'SH', source_table => 'PRODUCTS', column_type_list => 'PROD_ID NUMBER(6), PROD_NAME VARCHAR2(50), PROD_LIST_PRICE NUMBER(8,2), JOB_ID VARCHAR2(10), DEPARTMENT_ID NUMBER(4)', capture_values => 'both', rs_id => 'y', row_id => 'n', ... options_string => 'TABLESPACE TS_CHICAGO_DAILY'); END;

  28. Asynchronous Distributed HotLog Publishing • Enable the change source and change set: 1. Enable the change source. 2. Enable the change set. 3. Grant access to subscribers. BEGIN DBMS_CDC_PUBLISH.ALTER_HOTLOG_CHANGE_SOURCE( change_source_name => 'CHICAGO', enable_source => 'Y'); END; BEGIN DBMS_CDC_PUBLISH.ALTER_CHANGE_SET( change_set_name => 'CHICAGO_DAILY', enable_capture => 'y'); END;

  29. Subscribing to Change Data 1. Find the source tables for which the subscriber has access privileges. 2. Find the change set names and columns for which the subscriber has access privileges. SQL> SELECT * FROM ALL_SOURCE_TABLES; SOURCE_SCHEMA_NAME SOURCE_TABLE_NAME ------------------ ------------------ SH PRODUCTS SQL> SELECT UNIQUE CHANGE_SET_NAME, COLUMN_NAME, PUB_ID FROM 2 ALL_PUBLISHED_COLUMNS WHERE SOURCE_SCHEMA_NAME ='SH' AND 3 SOURCE_TABLE_NAME = 'PRODUCTS'; CHANGE_SET_NAME COLUMN_NAME PUB_ID ---------------- ------------------ ------------ CHICAGO_DAILY PROD_ID 41494 CHICAGO_DAILY PROD_LIST_PRICE 41494 CHICAGO_DAILY PROD_NAME 41494

  30. Subscribing to Change Data BEGIN DBMS_CDC_SUBSCRIBE.CREATE_SUBSCRIPTION( change_set_name => 'CHICAGO_DAILY', description => 'Change data for PRODUCTS', subscription_name => 'SALES_SUB'); END; 3. Create a subscription. 4. Subscribe to a source table and columns. BEGIN DBMS_CDC_SUBSCRIBE.SUBSCRIBE( subscription_name => 'SALES_SUB', source_schema => 'SH', source_table => 'PRODUCTS', column_list => 'PROD_ID, PROD_NAME, PROD_LIST_PRICE', subscriber_view => 'SALES_VIEW'); END;

  31. Subscribing to Change Data 5. Activate the subscription. 6. Get the next set of change data. BEGIN DBMS_CDC_SUBSCRIBE.ACTIVATE_SUBSCRIPTION( subscription_name => 'SALES_SUB'); END; BEGIN DBMS_CDC_SUBSCRIBE.EXTEND_WINDOW( subscription_name => 'SALES_SUB'); END;

  32. Subscribing to Change Data 7. Query the subscriber views. 8. Indicate that the change data is no longer needed. 9. End the subscription. SELECT PROD_ID, PROD_NAME, PROD_LIST_PRICE FROM SALES_VIEW; PROD_ID PROD_NAME PROD_LIST_PRICE ------- -------------------------------- --------------- 30 And 2 Crosscourt Tee Kids 14.99 30 And 2 Crosscourt Tee Kids 17.66 10 Gurfield& Murks Pleated Trousers 17.99 10 Gurfield& Murks Pleated Trousers 21.99 BEGIN DBMS_CDC_SUBSCRIBE.PURGE_WINDOW( subscription_name => 'SALES_SUB'); END;

  33. Parameter • Value • COMPATIBLE 10.2.0 or 10.0.0 • GLOBAL_NAMES TRUE • JOB_QUEUE_PROCESSES Maximum number of DBMS_JOB jobs that can run simultaneously plus 2 • OPEN_LINKS Should be equal to the number of Distributed HotLog change sources planned • PARALLEL_MAX_SERVERS The current value + (3 times the number of change sources planned) • PROCESSES The current value + (4 times the number of change sources planned) • SESSIONS The current value + (the number of change sources planned) • UNDO_RETENTION 3600 Asynchronous Distributed HotLog Source Database Initialization Parameters • For all Oracle Database 10g releases:

  34. Parameter • Value • COMPATIBLE 9.2.0 • GLOBAL_NAMES TRUE • JOB_QUEUE_PROCESSES Maximum number of DBMS_JOB jobs that can run simultaneously plus 2 • LOG_PARALLELISM 1 LOGMNR_MAX_PERSISTENT_SESSIONS The number of change sources planned • OPEN_LINKS The number of Distributed HotLog change sources planned • PARALLEL_MAX_SERVERS The current value + (3 times the number of change sources planned) • PROCESSES The current value + (the number of change sources planned) Asynchronous Distributed HotLog Source Database Initialization Parameters • For Oracle 9.2 databases:

  35. Parameter • Value • COMPATIBLE 10.2.0 • GLOBAL_NAMES TRUE • JAVA_POOL_SIZE 50000000 • OPEN_LINKS Equal to the number of Distributed HotLog change sources planned, but no less than 4 • PARALLEL_MAX_SERVERS The current value + (2 times the number of change sources planned) • PROCESSES The current value + (3 times the number of change sources planned) • SESSIONS The current value + (the number of change sources planned) • STREAMS_POOL_SIZE Set to the current value + ((the number of change sources planned) * (11MB)) Asynchronous Distributed HotLog Staging Database Initialization Parameters • For Oracle Database 10g Release 2:

  36. Data Dictionary Views Supporting CDC • CHANGE_SOURCES lists existing change sources. • CHANGE_SETS lists existing change sets. • CHANGE_PROPAGATIONS describes the streams propagation associated with a given distributed HotLog change source on the source database. • CHANGE_TABLES lists existing change tables. • DBA_SOURCE_TABLES lists published source tables. • DBA_PUBLISHED_COLUMNS lists published source table columns. • DBA_SUBSCRIPTIONS lists all registered subscriptions. • DBA_SUBSCRIBED_TABLES lists published tables to which subscribers have subscribed. • DBA_SUBSCRIBED_COLUMNS lists the columns of tables to which subscribers have subscribed.

  37. Transportation in a Data Warehouse • Three basic choices in transportation: • Transportation using flat files • Transportation through distributed operations • Transportation using transportable tablespaces

  38. Transportable Tablespaces • This is the fastest method for moving large volumes of data. • Source and target databases can have different block sizes. • The method is especially useful for transporting data from OLTP to data warehouse. • Before Oracle Database 10g, source and target databases needed to use the same operating system.

  39. Transportable Tablespaces: Example 1. Place the data into its own tablespace. 2. Export the metadata. 3. Copy the data and export file to the target system. 4. Import the metadata. 5. Insert the new data into the fact table or employ the partition exchange feature. CREATE TABLE temp_jan_sales NOLOGGING TABLESPACE ts_temp_sales AS SELECT * FROM sales WHERE time_id BETWEEN '31-DEC-1999' AND '01-FEB-2000'; EXPDP DIRECTORY=DW_DUMP_DIR DUMPFILE=jan.dmp TRANSPORT_TABLESPACES=ts_temp_sales IMPDP DIRECTORY=DM_DUMP_DIR DUMPFILE=jan.dmp TRANSPORT_DATAFILES='/db/tempjan.f'

  40. Summary • In this lesson, you should have learned how to: • Describe the core ETL framework inside the database and its integration advantage • Explain data warehousing extraction methods • Identify transportation methods: • Flat file • Distributes operations • Transportable tablespaces • Describe transformation flow

  41. Practice 4: Overview • This practice covers the following topics: • Loading data from a flat file by using SQL*Loader • Configuring synchronous Change Data Capture • Loading data from a transportable tablespace by using Data Pump

More Related