Introduction to Knowledge Modules

1) What is a Knowledge Module?

Knowledge Modules (KMs) are code templates. Each KM is dedicated to an individual task in the overall data integration process. The code in the KMs appears in nearly the form that it will be executed except that it includes Oracle Data Integrator (ODI) substitution methods enabling it to be used generically by many different integration jobs. The code that is generated and executed is derived from the declarative rules and metadata defined in the ODI Designer module.

2)  Types of Knowledge Modules:

2.1) Reverse-Engineering Knowledge Modules (RKM)

The RKM role is to perform customized reverse engineering for a model. The RKM is in charge of connecting to the application or meta-data provider then transforming and writing the resulting meta-data into Oracle Data Integration's repository. The meta-data is written temporarily into the SNP_REV_xx tables. The RKM then calls the Oracle Data Integrator API to read from these tables and write to Oracle Data Integrators meta-data tables of the work repository in incremental update mode. This is illustrated below

 A typical RKM follows these steps:

a).Cleans up the SNP_REV_xx tables from previous executions using the OdiReverseResetTable tool.

b). Retrieves sub models, datastores, columns, unique keys, foreign keys, conditions from the metadata provider to SNP_REV_SUB_MODEL, SNP_REV_TABLE,SNP_REV_COL, SNP_REV_KEY,SNP_REV_KEY_COL, SNP_REV_JOIN, SNP_REV_JOIN_COL, SNP_REV_COND tables.

C). Updates the model in the work repository by calling the OdiReverseSetMetaData tool.


Available RKM in ODI 11.1.1

1)  RKM DB2/400
2)  RKM FILE (FROM EXCEL)
3)  RKM HYPERION ESSBASE
4)  RKM FINANCIAL MANAGEMENT
5)  RKM HYPERION PLANNING
6)  RKM INFORMIX
7)  RKM INFORMIX SE
8)  RKM MSSQL
9)  RKM ORACLE
10) RKM ORACLE (JYOTHON)
11) RKM ORACLE DATA QUALITY
12) RKM ORACLE OLAP (JYOTHON)
13) RKM SQL (JYOTHON)
14) RKM TERADATA

The CKM accepts a set of constraints and the name of the table to check. It creates an "E$" error table which it writes all the rejected records to. The CKM can also remove the erroneous records from the checked result set.

The following figures show how a CKM operates in both STATIC_CONTROL and FLOW_CONTROL modes.

2.2) Check Knowledge Modules (CKM)

The CKM is in charge of checking that records of a data set are consistent with defined constraints. The CKM is used to maintain data integrity and participates in the overall data quality initiative. The CKM can be used in 2 ways:

a)    To check the consistency of existing data. This can be done on any datastore or within interfaces, by setting the STATIC_CONTROL option to "Yes". In the first case, the data checked is the data currently in the datastore. In the second case, data in the target datastore is checked after it is loaded.


b)    To check consistency of the incoming data before loading the records to a target datastore. This is done by using the FLOW_CONTROL option. In this case, the CKM simulates the constraints of the target datastore on the resulting flow prior to writing to the target

(STATIC_CONTROL)

 

  In STATIC_CONTROL mode, the CKM reads the constraints of the table and checks them against the data of the table. Records that don't match the constraints are written to the "E$" error table in the staging area.

(FLOW_CONTROL)

 

In FLOW_CONTROL mode, the CKM reads the constraints of the target table of the Interface. It checks these constraints against the data contained in the "I$" flow table of the staging area. Records that violate these constraints are written to the "E$" table of the staging area.

In both cases, a CKM usually performs the following tasks:

a) Create the "E$" error table on the staging area. The error table should contain the same columns as the datastore as well as additional columns to trace error messages, check origin, check date etc.

b) Isolate the erroneous records in the "E$" table for each primary key, alternate key, foreign key, condition, mandatory column that needs to be checked.

c) If required, remove erroneous records from the table that has been checked

Available CKM in ODI 11.1.1

 
a) CKM HSQL
b) CKM NETEZZA
c) CKM ORACLE
d) CKM SQL
e) CKM SYBASE IQ
f) CKM TERADATA
 

2.3) Loading Knowledge Modules (LKM)

An LKM is in charge of loading source data from a remote server to the staging area. It is used by interfaces when some of the source datastores are not on the same data server as the staging area. The LKM implements the declarative rules that need to be executed on the source server and retrieves a single result set that it stores in a "C$" table in the staging area, as illustrated below.

 

The LKM creates the "C$" temporary table in the staging area. This table will hold records loaded from the source server

a) The LKM obtains a set of pre-transformed records from the source server by executing the appropriate transformations on the source. Usually, this is done by a single SQL SELECT query when the source server is an RDBMS. When the source doesn't have SQL capacities (such as flat files or applications), the LKM simply reads the source data with the appropriate method (read file or execute API).

b) The LKM loads the records into the "C$" table of the staging area.

An interface may require several LKMs when it uses datastores from different sources. When all source datastores are on the same data server as the staging area, no LKM is required.

 Available LKM in ODI 11.1.1

1)    LKM ATUNITY TO SQL
2)    LKM DB2 400 JOURNAL TO SQL
3)    LKM DB2 400 JOURNAL TO DB2 400
4)    LKM DB2 UDB to DB2 UDB (EXPORT_IMPORT)
5)    LKM File to DB2 UDB (LOAD)
6)    LKM File to MSSQL (BULK)
7)    LKM File to Netezza (EXTERNAL TABLE)
8)    LKM File to Netezza (NZLOAD)
9)    LKM File to Oracle (EXTERNAL TABLE)
10)    LKM File to Oracle (SQLLDR)
11)    LKM File to SQL
12)    LKM File to Sybase IQ (LOAD TABLE)
13)    LKM File to Teradata (TTU)
14)    LKM Hyperion Essbase DATA to SQL
15)    LKM Hyperion Essbase METADATA to SQL
16)    LKM Hyperion Financial Management Data to SQL
17)    LKM Hyperion Financial Management Members To SQL
18)    LKM Informix to Informix (SAME SERVER)
19)    LKM JMS to SQL
20)    LKM JMS XML to SQL
21)    LKM MSSQL to MSSQL (BCP)
22)    LKM MSSQL to MSSQL (LINKED SERVERS)
23)    LKM MSSQL to ORACLE (BCP SQLLDR)
24)    LKM MSSQL to SQL (ESB XREF)
25)    LKM Oracle BI to Oracle (DBLINK)
26)    LKM Oracle BI to SQL
27)    LKM Oracle to Oracle (data pump)
28)    LKM Oracle to Oracle (DBLINK)
29)    LKM SQL to DB2 400 (CPYFRMIMPF)
30)    LKM SQL to DB2 UDB
31)    LKM SQL to DB2 UDB (LOAD)
32)    LKM SQL to MSSQL
33)    LKM SQL to MSSQL (BULK)
34)    LKM SQL to Oracle
35)    LKM SQL to SQL
36)    LKM SQL to SQL (ESB XREF)
37)    LKM SQL to SQL (JYTHON)
38)    LKM SQL to SQL (row by row)
39)    LKM SQL to Sybase ASE
40)    LKM SQL to Sybase ASE (BCP)
41)    LKM SQL to Sybase IQ (LOAD TABLE)
42)    LKM SQL to Teradata (TTU)
43)    LKM Sybase ASE to Sybase ASE (BCP) 

2.4) Integration Knowledge Modules (IKM)

The IKM is in charge of writing the final, transformed data to the target table. Every interface uses a single IKM. When the IKM is started, it assumes that all loading phases for the remote servers have already carried out their tasks. This means that all remote source data sets have been loaded by LKMs into "C$" temporary tables in the staging area, or the source datastores are on the same data server as the staging area.

Therefore, the IKM simply needs to execute the "Staging and Target" transformations, joins and filters on the "C$" tables, and tables located on the same data server as the staging area. The resulting set is usually processed by the IKM and written into the "I$" temporary table before loading it to the target. These final transformed records can be written in several ways depending on the IKM selected in your interface. They may be simply appended to the target, or compared for incremental updates or for slowly changing dimensions. There are 2 types of IKMs: those that assume that the staging area is on the same server as the target datastore, and those that can be used when it is not. These are illustrated below:

(Staging Area on Target)

 

When the staging area is on the target server, the IKM usually follows these steps:

a) The IKM executes a single set-oriented SELECT statement to carry out staging area and target declarative rules on all "C$" tables and local tables (such as D in the figure). This generates a result set.

b) Simple "append" IKMs directly write this result set into the target table. More complex IKMs create an "I$" table to store this result set.

c) If the data flow needs to be checked against target constraints, the IKM calls a CKM to isolate erroneous records and cleanse the "I$" table.

d) The IKM writes records from the "I$" table to the target following the defined strategy (incremental update, slowly changing dimension, etc.).

e) The IKM drops the "I$" temporary table.

f) Optionally, the IKM can call the CKM again to check the consistency of the target datastore.

These types of KMs do not manipulate data outside of the target server. Data processing is set-oriented for maximum efficiency when performing jobs on large volumes.

(Staging Area Different from Target) 

 

 

When the staging area is different from the target server, as shown in Figure, the IKM usually follows these steps:

a)  The IKM executes a single set-oriented SELECT statement to carry out declarative rules on all "C$" tables and tables located on the staging area (such as D in the figure). This generates a result set. 

b) The IKM loads this result set into the target datastore, following the defined strategy (append or incremental update).

This architecture has certain limitations, such as:

A CKM cannot be used to perform a data integrity audit on the data being processed.

Data needs to be extracted from the staging area before being loaded to the target, which may lead to performance issues.

Available IKM in ODI 11.1.1

1)      IKM Access Incremental Update

2)      IKM DB2 400 Incremental Update

3)      IKM DB2 400 Incremental Update (CPYF)

4)      IKM DB2 400 Slowly Changing Dimension

5)      IKM DB2 UDB Incremental Update

6)      IKM DB2 UDB Slowly Changing Dimension

7)      IKM File to Teradata (TTU)

8)      IKM Informix Incremental Update

9)      IKM MSSQL Incremental Update

10)   IKM MSSQL Slowly Changing Dimension

11)   IKM Netezza Control Append

12)   IKM Netezza Incremental Update

13)   IKM Netezza To File (EXTERNAL TABLE)

14)   IKM Oracle AW Incremental Update

15)   IKM Oracle BI to SQL Append

16)   IKM Oracle Incremental Update

17)   IKM Oracle Incremental Update (MERGE)

18)   IKM Oracle Incremental Update (PL SQL)

19)   IKM Oracle Multi Table Insert

20)   IKM Oracle Slowly Changing Dimension

21)   IKM Oracle Spatial Incremental Update

22)   IKM SQL Control Append

23)   IKM SQL Control Append (ESB XREF)

24)   IKM SQL Incremental Update

25)   IKM SQL Incremental Update (row by row)

26)   IKM SQL to File Append

27)   IKM SQL to Hyperion Essbase (DATA)

28)   IKM SQL to Hyperion Essbase (METADATA)

29)   IKM SQL to Hyperion Financial Management Data

30)   IKM SQL to Hyperion Financial Management Dimension

31)   IKM SQL to Hyperion Planning

32)   IKM SQL to JMS Append

33)   IKM SQL to JMS XML Append

34)   IKM SQL to SQL Append

35)   IKM SQL to Teradata (TTU)

36)   IKM Sybase ASE Incremental Update

37)   IKM Sybase ASE Slowly Changing Dimension

38)   IKM Sybase IQ Incremental Update

39)   IKM Sybase IQ Slowly Changing Dimension

40)   IKM Teradata Control Append

41)   IKM Teradata Incremental Update

42)   IKM Teradata Multi Statement

43)   IKM Teradata Slowly Changing Dimension

44)   IKM Teradata to File (TTU)

45)   IKM XML Control Append

 
2.5) Journalizing Knowledge Modules (JKM)

JKMs create the infrastructure for Change Data Capture on a model, a sub model or a datastore. JKMs are not used in interfaces, but rather within a model to define how the CDC infrastructure is initialized. This infrastructure is composed of a subscribers table, a table of changes, views on this table and one or more triggers or log capture programs as illustrated below.

Available JKM in ODI 11.1.1

1)    JKM DB2 400 Consistent
2)    JKM DB2 400 Simple
3)    JKM DB2 400 Simple (Journal)
4)    JKM DB2 UDB Consistent
5)    JKM DB2 UDB Simple
6)    JKM HSQL Consistent
7)    JKM HSQL Simple
8)    JKM Informix Consistent
9)    JKM Informix Simple
10)    JKM MSSQL Consistent
11)    JKM MSSQL Simple
12)    JKM Oracle 10g Consistent (Streams)
13)    JKM Oracle 11g Consistent (Streams)
14)    JKM Oracle Consistent
15)    JKM Oracle Consistent (Update Date)
16)    JKM Oracle Simple
17)    JKM Oracle to Oracle Consistent (OGG)
18)    JKM Sybase ASE Consistent
19)    JKM Sybase ASE Simple

2.6) Service Knowledge Modules (SKM)

SKMs are in charge of creating and deploying data manipulation Web Services to your Service Oriented Architecture (SOA) infrastructure. SKMs are set on a Model. They define the different operations to generate for each datastores web service. Unlike other KMs, SKMs do no generate an executable code but rather the Web Services deployment archive files. SKMs are designed to generate Java code using Oracle Data Integrator's framework for Web Services. The code is then compiled and eventually deployed on the Application Server's containers.


Available SKM in ODI 11.1.1

1)    SKM HSQL
2)    SKM IBM UDB
3)    SKM Informix
4)    SKM Oracle

Working With Change Data Capture

Changed Data Capture

Purpose of CDC is to enable applications to process changed data only. CDC enables ODI to track changes in source data caused by other applications. When running integration interfaces, ODI can avoid processing unchanged data in the flow. Loads will process only changes since the last load. The volume of data to be processed is dramatically reduced.

Reducing the source data flow to only changed data is useful in many contexts, such as data synchronization and replication. It is essential when setting up an event-oriented architecture for integration. In such architecture, applications make changes in the data ("Customer Deletion", "New Purchase Order") during a business process. These changes are captured by Oracle Data Integrator and transformed into events that are propagated throughout the information system.

CDC Techniques

1)Trigger based: ODI will create and maintain triggers to keep track of the changes.

2) Logs based: ODI retrieves changes from the database logs (Oracle, AS/400).

3) Time stamp based: Processes written with ODI can filter the data by comparing the time stamp value with the last load time (cannot process deletes)

4) Sequence number: If the records are numbered in sequence, ODI can filter the data based on the last value loaded (cannot process updates and deletes).

Changed Data Capture is performed by journalizing models. Journalizing a model consists of setting up the infrastructure to capture the changes (inserts, updates and deletes) made to the records of this model's datastores.
Oracle Data Integrator supports two journalizing modes:

•Simple Journalizing tracks changes in individual datastores in a model.
•Consistent Set Journalizing tracks changes to a group of the model's datastores, taking into account the referential integrity between these datastores. The group of datastores journalized in this mode is called a Consistent Set.

The Journalizing Components

The journalizing components are:
•Journals: Where changes are recorded. Journals only contain references to the changed records along with the type of changes (insert/update, delete).

•Capture processes: Journalizing captures the changes in the source datastores either by creating triggers on the data tables, or by using database-specific programs to retrieve log data from data server log files.

•Subscribers: CDC uses a publish/subscribe model. Subscribers are entities (applications, integration processes, etc) that use the changes tracked on a datastore or on a consistent set. They subscribe to a model's CDC to have the changes tracked for them. Changes are captured only if there is at least one subscriber to the changes. When all subscribers have consumed the captured changes, these changes are discarded from the journals.

•Journalizing views: Provide access to the changes and the changed data captured. They are used by the user to view the changes captured, and by integration processes to retrieve the changed data.
These components are implemented in the journalizing infrastructure

Setting up Journalizing:

This is the basic process for setting up CDC on an Oracle Data Integrator data model. Each of these steps is described in more detail below.

1.Set the CDC parameters
2.Add the datastores to the CDC
3.For consistent set journalizing, arrange the datastores in order
4.Add subscribers
5.Start the journals

Journalizing Tools:

Oracle Data Integrator provides a set of tools that can be used in journalizing to refresh information on the captured changes or trigger other processes:

•SnpsWaitForData waits for a number of rows in a table or a set of tables.

•SnpsWaitForLogData waits for a certain number of modifications to occur on a journalized table or a list of journalized tables. This tool calls SnpsRefreshJournalCount to perform the count of new changes captured.

•SnpsWaitForTable waits for a table to be created and populated with a pre-determined number of rows.

•SnpsRetrieveJournalData retrieves the journalized events for a given table list or CDC set for a specified journalizing subscriber. Calling this tool is required if using Database-Specific Processes to load journalizing tables. This tool needs to be used with specific knowledge modules.

•SnpsRefreshJournalCount refreshes the number of rows to consume for a given table list or CDC set for a specified journalizing subscriber.

Implementing Changed Data Capture:

Step:1) Import the appropriate JKM in the project.
Click the Projects tab. Expand the Procedure-Demo > Knowledge Modules node, right-click Journalization (JKM), and select Import Knowledge Modules.

Step:2) In the Models tab, create a new model named Oracle_relational_01. For Technology, enter: Oracle. Select the logical schema Sales_Order. Click the Reverse Engineer tab and set Context to development. Verify the setting, as shown in the following screen. Click the Journalizing tab.

Step: 3) In the Knowledge Module menu, select JKM Oracle Simple. Procedure-Demo, as shown in the following screen. Click the Save to save your model and then close the tab.

Step: 4) Reverse-engineer the model Oracle_Relational_01. Expand this model and verify its structure as follows.

Step: 5) Set up the CDC Infrastructure. You will start the CDC on the EMPLOYEE table in the Oracle_Relational_01 model.

To add the table to CDC, expand the Oracle_Relational_01 model, right-click the EMPLOYEE table, and select Change Data Capture > Add to CDC. Click Yes to confirm.

Step: 6) Click the Refresh icon. The small yellow clock icon is added to the table.

Step: 7) Right-click the EMPLOYEE table again and select Changed Data Capture > Start Journal.

Step: 8) you use the default subscriber SUNOPSIS. For that reason, you do not have to add another subscriber. Click OK to confirm that your subscriber is SUNOPSIS. In the Information window, click OK again. Wait several seconds, then click Refresh and verify that the tiny clock icon at the EMPLOYEE table is green now. This means that your journal has started properly.

Step: 9) Click the ODI Operator icon to open the Operator. Click Refresh. Select All Executions and verify that the EMPLOYEE session executed successfully.

Step: 10) View the data and the changed data.
In the Designer window, open the Models tab. Right-click the EMPLOYEE datastore and select Data.

Step: 11) Select the row with Employee_Key = 10. Change the value of the NAME2 column to “Symond”. Similarly, select the row with Employee_Key = 15, and then change the value to “jacob”. Save your changes and close the tab.

Step: 12) Right-click the table again and select View Data. Scroll down, and verify that the rows are modified. Close the tab.

To verify that your changed data is captured, right-click EMPLOYEE, and select Change Data Capture > Journal Data. Find the captured changed records in the journal data. Close the tab.

Done !