dbms-notes: writing blocks to disk

Transforming presentation: What is a Pivoting operation?

It is a key technique in DWH: allow you to transform multiple rows of input into fewer and generally wider rows

Allow to transform the presentation of the data, rotating rows into columns and presenting data in a crosstabular format, or vice-versa.

When pivoting, an aggregation operator is applied for each item in the pivot column value list

Use the pivot_clause of the SELECT statement to write crosstabulation queries that rotate rows into columns, aggregating data in the process of the rotation.

Pivoting: Transactional format <=> Crosstabular (relational) format

Consider the example below:

The view SALES_VIEW displays data in a transactional format

It lists amount and quantity sold by product, country, quarter and channel.

In this case, a product may be sold through various channels in a given country and a given quarter of year.

If you need to see the sales by channel_id on a relational or crosstabular format, you can use a pivoting operation:

SQL> CREATE VIEW sales_view AS
 SELECT
   prod_name product, country_name country, channel_id channel,
   SUBSTR(calendar_quarter_desc, 6,2) quarter,
   SUM(amount_sold) amount_sold, SUM(quantity_sold) quantity_sold
 FROM sales, times, customers, countries, products
 WHERE sales.time_id = times.time_id AND
    sales.prod_id = products.prod_id AND
   sales.cust_id = customers.cust_id AND
   customers.country_id = countries.country_id
 GROUP BY prod_name, country_name, channel_id,
 SUBSTR(calendar_quarter_desc, 6, 2);

Using the PIVOT clause you can reconstruct the data on a cross tabular form:

SQL> SELECT * FROM
 (SELECT product, channel, amount_sold
   FROM sales_view
   WHERE country = 'United States of America' 
 ) S PIVOT (SUM(amount_sold)
   FOR CHANNEL IN (3 AS DIRECT_SALES, 4 AS INTERNET_SALES,
                   5 AS CATALOG_SALES, 9 AS TELESALES))
ORDER BY product;

PRODUCT                   DIRECT_SALES           INTERNET_SALES         CATALOG_SALES          TELESALES           
------------------------- ---------------------- ---------------------- ---------------------- ------------------ 
...
Home Theatre Package...   2052150.1              310431.22                                                            
Internal 6X CD-ROM        135284.16              15839.73                                                             
Internal 8X CD-ROM        158157.23              24398.22                                                             
Keyboard Wrist Rest       110554                 22477.41                                      1354.87                
...
 71 rows selected

Data Integration and ODI - Concepts

Integration projects can be:

Data Oriented, especially those involving large data volumes.
Event Oriented - Based on an Event Driven Architecture (EDA) or a
Service Oriented - Suitable to a Service Oriented Architecture (SOA), for asynchronous or synchronous integration.

Oracle Data Integrator:

combines all the elements of data integration: data movement, data synchronization, data quality, data management, and data services.
transforms large volumes of data efficiently,
processes events in real time through its advanced Changed Data Capture (CDC) capability, and
provides data services to the Oracle SOA Suite.
provides robust data integrity control features, assuring the consistency and correctness of data

ODI Studio - Interface and Concepts

Declarative Design

Declarative Design is a design method that focuses on “What” to do (the Declarative Rules) rather than “How” to do it (the Process).

Declarative Design in ODI uses the well known relational paradigm to declare in the form of an Interface the declarative rules for a data integration task, which includes designation of sources, targets, and transformations.

Declarative rules often apply to metadata to transform data and are usually described in natural language by business users.

In a typical data integration project (such as a Data Warehouse project), these rules are defined during the specification phase in documents written by business analysts in conjunction with project managers.

They can very often be implemented using SQL expressions, provided that the metadata they refer to is known and qualified in a metadata repository.

Types of declarative rules: Mappings, Joins, Filters and Constraints

Integration Project

Suggested steps for the life cycle of an integration project:
1. Create a new project and import Knowledge Modules.
2. Define the project organization and practises using folders, markers and documentation.
3. Create reusable components: interfaces, procedures, variables, sequences. Perform unitary tests.
4. Assemble these components into packages. Perform integration tests.
5. Release the work in scenarios

Package
Package is a workflow, made up of a sequence of steps organized into an execution diagram.
Each step can either succeed or fail its execution.
Packages assemble and reference other component from a project (interfaces, procedures, variables, etc)
Types of steps: Flow (Interface), Procedue, Variable, ODI tools (allow access to API commands, OS system calls), models, sub-models, and Datastores.

The Package above (Load Customers) specifies a workflow that loads a sequence of tables:

(1) Initialization step: Execute procedure Initialization (type Procedure)
(2) Execute interface Load Countries (type: Flow(interface)), linked object: TRG_COUNTRY
(3) Execute interface Load Regions (type: Flow(interface)), linked object: TRG_REGION
(4) Execute interface Load Cities (type: Flow(interface)), linked object: TRG_CITY
(5) refresh variable "CUST_FLAG". Load Customers? (type: Evaluate Variable), linked objects: CUST_FLAG
IF CUST_FLAG = 1 (true) THEN Load Customers, ELSE Final Step
(6) Final Step (type procedure)
(7) If any of the steps above fails,the Package runs the "Send Admin email" step that sends an email using an ODI tool.

Interface
It is set of declarative rules that describe the loading of a datastore or a temporary target structure from one or more source datastores.
An interface is a reusable dataflow.

Components of an Integration Interface:

Target Datastore - element that will be loaded by the interface.

May be permanent (defined in a model) or temporary (created by the interface)

Datasets - One target is loaded with data coming from various datasets

each dataset corresponds to one diagram of source datastores and the mappings used to load the target datastore from these source datastores.

Diagram of Source Datastores

Mapping - defines the transformations performed on one or various source coluns to load one target column

Staging area

Flow

Control

In the example above,

The interface defines a set of rules to load the target TRG_CUSTOMER Datastore with data coming from three source datastores (SRC_AGEGROUP, SRC_CUSTOMER, SRC_SALES_PERSON)

It defines a Join between SRC_CUSTOMER and SRC_SALES_PERSON

it defines a Lookup: CUST.AGE BETWEEN AGE_GROUP_AGE_MIN and AGE_GROUP.AGE_MAX

Define Mappings between the attributes in the sources datastores and attributes in the target datastore

Lookup
Join
Mapping

Interface dataflow

Automatically generated by ODI from the declarative rules in the interface

Based on the configuration files and context (target, sources, where is the staging area, etc)

ODI automatically computes the flow depending on the configuration in the interface's diagram.

It proposes default KMs for the data flow.

The Flow tab enables you to view the data flow and select the KMs used to load and integrate data.

In the flow, the following items appear:
Source Sets:

Source Datastores that are within the same dataset, located on the same physical data server and which are joined with Joins located on the Source are grouped in a single source set in the flow diagram.

A source set represents a group of datastores that can be extracted at the same time.

DataSets:

Datasets appear as yellow boxes in the Staging Area.

Staging Area:

Appears as a box that includes the different datasets, the target (if located on the same data server), and possibly some of the sources (if located on
the same data server)

Target:

Appears as a separate box if it is located in a different schema from the staging area

You use the following KMs in the flow:

Loading KM (LKM):

Define how data is moved.

One LKM is selected for each Source Set for moving data from the sources to the staging area.

It can be also selected to move data from the Staging Area - when different from the Target - to the Target, when a
single technology IKM is selected for the Staging Area.

Integration KM (IKM): They define how data is integrated into the target. One IKM is typically
selected on the Target. When the staging area is different from the target, the
selected IKM can be a multi-technology IKM that moves and integrates data from
the Staging Area into the Target.

Procedure
A Procedure is a reusable component that groups a sequence of operations that do not fit in the interface concept.
Examples of procedures:
■ wait and unzip a file
■ send a batch of files via FTP
■ receive emails
■ purge a database

Knowledge Modules

Knowledge Modules (KM) implement “how” the integration processes occur
KMs are components of ODI Open Connector technology.
Contain the knowledge required by ODI to perform a specific set of tasks against a specific technology or set of technologies.
When starting an integration project, you must import the Knowledge Module appropriate for your project.
It is possible to import additional KMs after setting up the project, and it is possible to change the KMs used afterwards
Start with Generic KMs. The SQL KMs work with almost all technologies
Start with simple KMs.
Select KMs that match your source/target combinations to increase performance
Select KMs according to your infrastructure limitations
Select JKMs and SKMs only if you need them
Encrypting a Knowledge Module (KM) allows you to protect valuable code
Oracle Data Integrator uses a DES Encryption algorithm based on a personal encryption key.
This key can be saved in a file and can be reused to perform encryption or decryption operations.

ODI uses six (6) types of Knowledge Modules:
■ RKM (Reverse Knowledge Modules) Perform a customized reverse-engineering of data models for a specific technology.
■ RKMs are used in data models.

RKM <reversed technology> [(reverse method)]
i.e. RKM Oracle, RKM Netezza

■ LKM (Loading Knowledge Modules) are used to extract data from source systems (files, middleware, database, etc.).
■ LKMs are used in interfaces.

LKM <source technology> to <target technology> [(loading method)]
i.e. LKM Oracle to Oracle (DBLink), LKM File to Oracle (SQLLDR), LKM SQL to SQL, etc

■ JKM (Journalizing Knowledge Modules) are used to create a journal of data modifications (insert, update and delete) of the source databases to keep track of
the changes.
■ JKMs are used in data models and used for Changed Data Capture.

JKM <journalized technology> <journalizing mode> (<journalizing method>)
JKM Oracle11g Consistent (Streams)
JFM Oracle Simple
JKM DB2 400 Simple (journal)

■ IKM (Integration Knowledge Modules) are used to integrate (load) data to the target tables.
■ IKMs are used in interfaces.

IKM [<staging technology>] <target technology> [<integration mode>] [(<integration method>)].
i.e. IKM Oracle Incremental Update (MERGE)
IKM SQL to File Append
IKM SQL Incremental Update
IKM SQL to SQL Append

■ CKM (Check Knowledge Modules) are used to check that constraints on the sources and targets are not violated.
■ CKMs are used in data model’s static check and interfaces flow checks.

CKM <staging technology>
i.e. CKM SQL, CKM Oracle

■ SKM (Service Knowledge Modules) are used to generate the code required for creating data services.
■ SKMs are used in data models.

Pages

ON SQL functions

Data Warehouse: Concepts on ETL Processes (Pivoting) (II)

Oracle Data Intergrator (ODI)