Informatica PowerCenter provides integrated components including a repository to store metadata, a client with tools for designing mappings and workflows, and a server to extract, transform and load data between sources and targets based on the metadata in the repository and workflows created in the client. The designer tool allows importing of sources and targets, creating mappings through connecting them with transformations, and testing mappings.
Big Data Analytics in a Heterogeneous World - Joydeep Das of SybaseBigDataCloud
Big Data Analytics is characterized by analysis of data on three vectors: exploding data volume, proliferating data variety (relational, multi-media), and accelerating data velocity. However, other key vectors such as costs and skill set needed for Big Data Analytics are often overlooked. In this session, we will consider all five vectors by exploring various techniques where traditional but progressive technologies such as column store DBMS and Event Stream Processing is combined with open source frameworks such as Hadoop to exploit the full potential of Big Data Analytics.
Agenda:
- Big Data Analytics in the real world
- Commercial and Open Source techniques
- Bringing together Commercial and Open Source techniques
* Architectures
* Programming APIs
(e.g. embedded and federated MapReduce)
- Conclusions
Big Data Analytics in a Heterogeneous World - Joydeep Das of SybaseBigDataCloud
Big Data Analytics is characterized by analysis of data on three vectors: exploding data volume, proliferating data variety (relational, multi-media), and accelerating data velocity. However, other key vectors such as costs and skill set needed for Big Data Analytics are often overlooked. In this session, we will consider all five vectors by exploring various techniques where traditional but progressive technologies such as column store DBMS and Event Stream Processing is combined with open source frameworks such as Hadoop to exploit the full potential of Big Data Analytics.
Agenda:
- Big Data Analytics in the real world
- Commercial and Open Source techniques
- Bringing together Commercial and Open Source techniques
* Architectures
* Programming APIs
(e.g. embedded and federated MapReduce)
- Conclusions
A Data Scientist And A Log File Walk Into A Bar...Paco Nathan
Presented at Splunk .conf 2012 in Las Vegas. Includes an overview of the Cascading app based on City of Palo Alto open data. PS: email me if you need a different format than Keynote: @pacoid or pnathan AT concurrentinc DOT com
This PPT will help for SAP Interview Questions particularly SAP domain Candidates. for more information please login to www.rekruitin.com
By ReKruiTIn.com
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A presentation on the use of OneTick and the R mathematical and analytical language. This session presents the integration of OneTick and R in three use-case scenarios
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In SAP R/3 and SAP ECC, the Legacy System Migration Workbench is a tool that supports transferring data from (non-)SAP systems ("Legacy Systems") to SAP systems once or periodically.
SAP HANA and Apache Hadoop for Big Data Management (SF Scalable Systems Meetup)Will Gardella
In this presentation I argue that the future of data management may see a split between (1) real-time in-memory systems such as SAP HANA for most enterprise workloads (2) disk-based free and open-source Apache Hadoop for certain specialized big data uses.
The presentation starts with a definition of what is intended by the term big data, then talks about SAP HANA and Apache Hadoop from the perspective of suitability for enterprise use with a special concentration on Hadoop. (The basics of SAP HANA were covered in the immediately preceding session). This is followed by a description of currently available SAP support for Apache Hadoop in SAP BI 4.0 and SAP Data Services / EIM. Due to time constraints I did not discuss Apache Hadoop support built into Sybase IQ.
A Data Scientist And A Log File Walk Into A Bar...Paco Nathan
Presented at Splunk .conf 2012 in Las Vegas. Includes an overview of the Cascading app based on City of Palo Alto open data. PS: email me if you need a different format than Keynote: @pacoid or pnathan AT concurrentinc DOT com
This PPT will help for SAP Interview Questions particularly SAP domain Candidates. for more information please login to www.rekruitin.com
By ReKruiTIn.com
OneTick and the R mathematical language, a presentation from R in FinanceOneMarketData, LLC
A presentation on the use of OneTick and the R mathematical and analytical language. This session presents the integration of OneTick and R in three use-case scenarios
SAP Legacy System Migration Workbench (LSMW): IntroductionJonathan Eemans
In SAP R/3 and SAP ECC, the Legacy System Migration Workbench is a tool that supports transferring data from (non-)SAP systems ("Legacy Systems") to SAP systems once or periodically.
SAP HANA and Apache Hadoop for Big Data Management (SF Scalable Systems Meetup)Will Gardella
In this presentation I argue that the future of data management may see a split between (1) real-time in-memory systems such as SAP HANA for most enterprise workloads (2) disk-based free and open-source Apache Hadoop for certain specialized big data uses.
The presentation starts with a definition of what is intended by the term big data, then talks about SAP HANA and Apache Hadoop from the perspective of suitability for enterprise use with a special concentration on Hadoop. (The basics of SAP HANA were covered in the immediately preceding session). This is followed by a description of currently available SAP support for Apache Hadoop in SAP BI 4.0 and SAP Data Services / EIM. Due to time constraints I did not discuss Apache Hadoop support built into Sybase IQ.
50-55 hours Training + Assignments + Actual Project Based Case Studies
All attendees will receive,
Assignment after each module, Video recording of every session
Notes and study material for examples covered.
Access to the Training Blog & Repository of Materials
Hadoop World 2011: Data Ingestion, Egression, and Preparation for Hadoop - Sa...Cloudera, Inc.
One of the first challenges Hadoop developers face is accessing all the data they need and getting it into Hadoop for analysis. Informatica PowerExchange accesses a variety of data types and structures at different latencies (e.g. batch, real-time, or near real-time) and ingests data directly into Hadoop. The next step is to parse the data in preparation for analysis in Hadoop. Informatica provides a visual IDE to deploy pre-built parsers or design specific parsers for complex data formats and deploy them on Hadoop. Once the analysis is complete, Informatica PowerExhange delivers the resulting output to other information management systems such as a data warehouse. Learn in this session from Informatica and one of their customers, how to get all the data you need into Hadoop, parse a variety of data formats and structures, and egress the resultant output to other systems.
Trending use cases have pointed out the complementary nature of Hadoop and existing data management systems—emphasizing the importance of leveraging SQL, engineering, and operational skills, as well as incorporating novel uses of MapReduce to improve distributed analytic processing. Many vendors have provided interfaces between SQL systems and Hadoop but have not been able to semantically integrate these technologies while Hive, Pig and SQL processing islands proliferate. This session will discuss how Teradata is working with Hortonworks to optimize the use of Hadoop within the Teradata Analytical Ecosystem to ingest, store, and refine new data types, as well as exciting new developments to bridge the gap between Hadoop and SQL to unlock deeper insights from data in Hadoop. The use of Teradata Aster as a tightly integrated SQL-MapReduce® Discovery Platform for Hadoop environments will also be discussed.
Learn What is inforamatica powercenter and its uses, Informatica powercenter designer,informatica powercenter repository, oltp system informatica,olap system informatica,informatica powercenter etl processing
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
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We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
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An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
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This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
2. Data Warehousing
Data warehousing is the entire
process of data extraction,
transformation, and loading of data to
the warehouse and the access of the
data by end users and applications
3. Data Mart
A data mart stores data for a limited
number of subject areas, such as
marketing and sales data. It is used
to support specific applications.
An independent data mart is created
directly from source systems.
A dependent data mart is populated
from a data warehouse.
4. Data Sources ETL Software Data Stores Data Analysis Users
Tools and
Applications
Transaction Data S
T
IBM A
Prod G
I
N SQL
G ANALYSTS
Mkt IMS
A
R Cognos
Ascential E
HR VSAM Data Marts
A Teradata SAS
IBM MANAGERS
O Fi nance
Oracle P
Fi n E Load Data Essbase Queri es,Reporting,
R
Extract A Warehouse DSS/EIS,
T Informatica
Acctg Syba se Data Mining
I Marketing
O EXECUTIVES
Other Internal Data N Micro Strategy
A Meta
L Dat a Sales
ERP SAP Sagent
D
A Microsoft Si ebel
Web Data T
A Business OPERATIO NAL
Infor mix Objects PERSONNEL
Clickstream S
T
SAS O
External Data R Web
E Browser
Demographic Harte-
Hanks Clean/Scrub CUSTOMERS/
Trans form SUPPLIERS
Fi rst logic
5. Need For ETL Tool
Data Extraction
Often performed by COBOL routines
(not recommended because of high program
maintenance and no automatically generated
meta data)
Sometimes source data is copied to the
target database using the replication
capabilities of standard RDBMS (not
recommended because of “dirty data” in the
source systems)
Increasing performed by specialized ETL
software
6. Sample ETL Tools
DataStage from Ascential Software
SAS System from SAS Institute
Informatica
Data Integrator From BO
Hummingbird Genio Suite from Hummingbird
Communications
Oracle Express
Abinito
Decision Stream From Cognos
MS-DTS from Microsoft
8. Informatica provides the following integrated
components:
• Informatica repository. The Informatica repository is at
the center of the Informatica suite. You create a set of
metadata tables within the repository database that the
Informatica applications and tools access. The Informatica
Client and Server access the repository to save and
retrieve metadata.
• Informatica Client. Use the Informatica Client to manage
users, define sources and targets, build mappings and
mapplets with the transformation logic, and create sessions
to run the mapping logic. The Informatica Client has three
client applications: Repository Manager, Designer, and
Workflow Manager.
• Informatica Server. The Informatica Server extracts the
source data, performs the data transformation, and loads
the transformed data into the targets.
10. Process Flow
Informatica Server moves the data from source to target
based on the workflow and metadata stored in the
repository.
A workflow is a set of instructions how and when to run
the task related to ETL.
Informatica server runs workflow according to the
conditional links connecting tasks.
Session is type of workflow task which describes how to
move the data between source and target using a
mapping.
Mapping is a set of source and target definitions linked
by transformation objects that define the rules for data
transformation.
11. Sources
Power Mart and Power Center access the following sources:
• Relational. Oracle, Sybase, Informix, IBM DB2, Microsoft SQL
Server, and Teradata.
• File. Fixed and delimited flat file, COBOL file, and XML.
• Extended. If you use Power Center, you can purchase
additional Power Connect products to access business sources
such as PeopleSoft, SAP R/3, Siebel, and IBM MQSeries.
• Mainframe. If you use Power Center, you can purchase Power
Connect for IBM DB2 for faster access to IBM DB2 on MVS.
• Other. Microsoft Excel and Access.
12. Targets
Power Mart and Power Center can load data into the
following targets:
• Relational. Oracle, Sybase, Sybase IQ, Informix, IBM DB2,
Microsoft SQL Server, and Teradata.
• File. Fixed and delimited flat files and XML.
• Extended. If you use Power Center, you can purchase an
integration server to load data into SAP BW. You can also
purchase Power Connect for IBM MQSeries to load data into
IBM MQSeries message queues.
• Other. Microsoft Access.
You can load data into targets using ODBC or native drivers,
FTP, or external loaders.
13. General Flow of Informatica
Step 1: Creating Repository ,creating folders ,Creating
users and assign permission in Repository Manager, so
as to work in the client tools.
Step 2:Connecting to the repository from the designer.
importing source and target tables , creation of
mappings.
Step 3 : Creation of Workflow through workflow Manager
which has different tasks connected between them. In
that ,session is the task which is pointing to a mapping
created in the designer.
14. Repository
The Informatica repository is a set of tables that
stores the metadata you create using the Informatica
Client tools. You create a database for the repository,
and then use the Repository Manager to create the
metadata tables in the database.
You add metadata to the repository tables when you
perform tasks in the Informatica Client application
such as creating users, analyzing sources, developing
mappings or mapplets, or creating sessions. The
Informatica Server reads metadata created in the
Client application when you run a session. The
Informatica Server also creates metadata such as
start and finish times of a session or session status.
Contd :-
15. Repository Contd..
When you use Power Center, you can develop global and
local repository to share metadata:
Global repository. The global repository is the hub of the
domain. Use the global repository to store common objects that
multiple developers can use through shortcuts. These objects may
include operational or application source definitions, reusable
transformations, mapplets, and mappings.
Local repositories. A local repository is within a domain that is
not the global repository. Use local repositories for
development. From a local repository, you can create
shortcuts to objects in shared folders in the global repository.
These objects typically include source definitions, common
dimensions and lookups, and enterprise standard
transformations. You can also create copies of objects in non-
shared folders.
17. Creating a Repository
To create Repository
1. Launch the Repository Manager by choosing Programs-Power
Center (or Power Mart) Client-Repository Manager from the Start
Menu.
2. In the Repository Manager, choose Repository-Create
Repository.
Note: You must be running the Repository Manager in
Administrator mode to see the Create Repository option on the
menu. Administrator mode is the default when you install the
program.
3. In the Create Repository dialog box, specify the name of the
new repository, as well as the parameters needed to
connect to the repository database through ODBC.
18. Working with Repository..
By default 2 users will be created in the repository .
Database user used to connect to the repository.
Administrator User.
By default 2 Groups will be created
Public
Administrators.
These groups and users cannot be deleted from the
repository . The administrator group has only read
privilege for other user groups.
19. Working with Repository contd..
Informatica tools include two basic types of security:
• Privileges. Repository-wide security that
controls which task or set of tasks a single user
or group of users can access. Examples of these
are Use Designer, Browse repository , Session
operator etc.
• Permissions. Security assigned to individual
folders within the repository. You can perform
various tasks for each privilege.
Ex :- Read , Write and Execute.
20. Folders
Folders provide a way to organize and store all metadata in the
repository, including mappings, schemas, and sessions. Folders
are designed to be flexible, to help you organize your data
warehouse logically. Each folder has a set of properties you can
configure to define how users access the folder. For example,
you can create a folder that allows all repository users to see
objects within the folder, but not to edit them. Or you can
create a folder that allows users to share objects within the
folder.
Shared Folders
When you create a folder, you can configure it as a shared
folder. Shared folders allow users to create shortcuts to objects
in the folder. If you have reusable transformation that you want
to use in several mappings or across multiple folders, you can
place the object in a shared folder.
For example, you may have a reusable Expression
transformation that calculates sales commissions. You can then
use the object in other folders by creating a shortcut to the
object.
21. Folder Permissions
Permissions allow repository users to perform tasks within a
folder. With folder permissions, you can control user access
to the folder, and the tasks you permit them to perform.
Folder permissions work closely with repository privileges.
Privileges grant access to specific tasks while permissions
grant access to specific folders with read, write, and
execute qualifiers.
However, any user with the Super User privilege can
perform all tasks across all folders in the repository.
Folders have the following types of permissions:
• Read permission. Allows you to view the folder as well as
objects in the folder.
• Write permission. Allows you to create or edit objects in the
folder.
• Execute permission. Allows you to execute or schedule a
session or batch in the folder.
23. Other Features of Repository Manager
Viewing , removing Locks
Adding Repository
Backup and Recovery of Repository
Taking Metadata reports like Completed Sessions
details , List of reports on Jobs , session ,
workflow etc
26. Working with Designer
Connecting to the repository using User id
and password.
Accessing the folder
Importing the source and target tables
required for mapping.
Creation of mapping
27. Tools provided by Designer
Source Analyzer: Importing Source definitions
for Flat file, XML, COBOL and relational Sources.
Warehouse Designer: Use to Import or create
target definitions.
Transformation Developer: Used to create
reusable transformations
Mapplet Designer: Used to create mapplets
Mapping Designer: Used to create mappings
31. Creating Targets
You can create target definitions in the Warehouse Designer for file
and relational sources. Create definitions in the following ways:
• Import the definition for an existing target. Import the
target definition from a relational target.
• Create a target definition based on a source definition.
Drag one of the following existing source definitions into the
Warehouse Designer to make a target definition:
o Relational source definition
o Flat file source definition
o COBOL source definition
• Manually create a target definition. Create and design a
target definition in the Warehouse Designer.
34. Creation of simple mapping
Switch to the Mapping Designer.
Choose Mappings-Create.
While the workspace may appear blank, in fact it contains a new
mapping without any sources, targets, or transformations.
In the Mapping Name dialog box, enter <Mapping Name> as the name
of the new mapping and click OK.
The naming convention for mappings is m_MappingName.
In the Navigator, under the <Repository Name> repository and
<Folder Name> folder, click the Sources node to view source
definitions added to the repository.
Contd..
35. Mapping creation Contd..
Click the icon representing the EMPLOYEES source and drag
it into the workbook.
36. Mapping creation Contd..
The source definition appears in the workspace. The
Designer automatically connects a Source Qualifier
transformation to the source definition. After you add
the target definition, you connect the Source Qualifier to
the target.
Click the Targets icon in the Navigator to open the
list of all target definitions.
Click and drag the icon for the T_EMPLOYEES target
into the workspace.
The target definition appears. The final step is
connecting the Source Qualifier to this target
definition.
37. Mapping creation Contd..
To Connect the Source Qualifier to Target Definition:
Click once in the middle of the <Column Name> in the Source
Qualifier. Hold down the mouse button, and drag the cursor to the
<Column Name> in the target. Then release the mouse button.
An arrow (called a connector) now appears between the row
columns
39. Transformations
A transformation is a repository object that generates,
modifies, or passes data
The Designer provides a set of transformations that
perform specific functions
Data passes into and out of transformations through
ports that you connect in a mapping or mapplet
Transformations can be active or passive
40. Transformations
Active transformations
Aggregator performs aggregate calculations
Filter serves as a conditional filter
Router serves as a conditional filter (more than one filters)
Joiner allows for heterogeneous joins
Source qualifier represents all data queried from the source
Passive transformations
Expression performs simple calculations
Lookup looks up values and passes to other objects
Sequence generator generates unique ID values
Stored procedure calls a stored procedure and captures return values
Update strategy allows for logic to insert, update, delete, or reject
data
41. Transformations Contd..
Create the transformation. Create it in the Mapping
Designer as part of a mapping, in the Mapplet Designer as
part of a Mapplet, or in the Transformation Developer as
a reusable transformation.
Configure the transformation. Each type of transformation
has a unique set of options that you can configure.
Connect the transformation to other transformations
and target definitions. Drag one port to another to
connect them in the mapping or Mapplet.
42. Expression Transformation
You can use the Expression transformations to calculate
values in a single row before you write to the target.
For example, you might need to adjust employee salaries,
concatenate first and last names, or convert strings to
numbers.
You can use the Expression transformation to perform any
non-aggregate calculations.
You can also use the Expression transformation to test
conditional statements before you output the results to
target tables or other transformations.
43. Expression Transformation
Calculating Values
To use the Expression transformation to calculate values for a single
row, you must include the following ports:
Input or input/output ports for each value used in the
calculation. For example, when calculating the total price for an
order, determined by multiplying the unit price by the quantity
ordered, the input or input/output ports. One port provides the
unit price and the other provides the quantity ordered.
Output port for the expression. You enter the expression as a
configuration option for the output port. The return value for
the output port needs to match the return value of the
expression.
Variable Port : Variable Port is used like local variable inside
Expression Transformation , which can be used in other calculations
44. Source Qualifier Transformation
Every mapping includes a Source Qualifier
transformation, representing all the columns
of information read from a source and
temporarily stored by the Informatica Server.
In addition, you can add transformations such
as a calculating sum, looking up a value, or
generating a unique ID that modify
information before it reaches the target.
45. Source Qualifier Transformation
When you add a relational or a flat file source definition to a mapping, you need to connect
it to a Source Qualifier transformation.
The Source Qualifier represents the records that the Informatica Server reads when it runs a
session. You can use the Source Qualifier to perform the following tasks:
• Join data originating from the same source database. You can join two or more tables
with primary-foreign key relationships by linking the sources to one Source Qualifier.
• Filter records when the Informatica Server reads source data. If you include a filter
condition, the Informatica Server adds a WHERE clause to the default query.
• Specify an outer join rather than the default inner join. If you include a user-defined
join, the Informatica Server replaces the join information specified by the metadata in the
SQL query.
• Specify sorted ports. If you specify a number for sorted ports, the Informatica Server
adds an ORDER BY clause to the default SQL query.
• Select only distinct values from the source. If you choose Select Distinct, the
Informatica Server adds a SELECT DISTINCT statement to the default SQL query.
• Create a custom query to issue a special SELECT statement for the Informatica Server to
read source data. For example, you might use a custom query to perform aggregate
calculations or execute a stored procedure
46. Configuring Source Qualifier Transformation
To configure a Source Qualifier:
• In the Designer, open a mapping.
• Double-click the title bar of the Source Qualifier.
• In the Edit Transformations dialog box, click
Rename, enter a descriptive name for the
transformation, and click OK. The naming
convention for Source Qualifier transformations is
SQ_TransformationName,.
• Click the Properties tab.
47. Configuring Source Qualifier
Option Description
Defines a custom query that replaces the default query the Informatica Server uses
SQL Query
to read data from sources represented in this Source Qualifier
User-Defined Specifies the condition used to join data from multiple sources represented in the
Join same Source Qualifier transformation
Source Filter Specifies the filter condition the Informatica Server applies when querying records.
Indicates the number of columns used when sorting records queried from relational
Number of sources. If you select this option, the Informatica Server adds an ORDER BY to
Sorted the default query when it reads source records. The ORDER BY includes the
Ports number of ports specified, starting from the top of the Source Qualifier.
When selected, the database sort order must match the session sort order.
Sets the amount of detail included in the session log when you run a session
Tracing Level
containing this transformation.
Select Distinct Specifies if you want to select only unique records. The Informatica Server includes a
SELECT DISTINCT statement if you choose this option.
48. Joiner Transformation
While a Source Qualifier transformation can join data originating from a common source database,
the Joiner transformation joins two related
heterogeneous sources residing in different locations or file systems. The combination of sources
can be varied. You can use the following sources:
• Two relational tables existing in separate databases
• Two flat files in potentially different file systems
• Two different ODBC sources
• Two instances of the same XML source
• A relational table and a flat file source
• A relational table and an XML source
If two relational sources contain keys, then a Source Qualifier transformation can easily join the
sources on those keys. Joiner transformations typically combine information from two
different sources that do not have matching keys, such as flat file sources.
The Joiner transformation allows you to join sources that contain binary data.
49. Creating a Joiner Transformation
To create a Joiner Transformation:
• In the Mapping Designer, choose Transformation-Create. Select the
Joiner transformation. Enter a name for the Joiner. Click OK. The
naming convention for Joiner transformations is
JNR_TransformationName. Enter a description for the transformation.
This description appears in the Repository Manager, making it easier for
you or others to understand or remember what the transformation
does.
• The Designer creates the Joiner transformation. Keep in mind that you
cannot use a Sequence Generator or Update Strategy transformation as
a source to a Joiner transformation.
• Drag all the desired input/output ports from the first source into the
Joiner transformation. The Designer creates input/output ports for the
source fields in the Joiner as detail fields by default. You can edit this
property later.
• Select and drag all the desired input/output ports from the second
source into the Joiner transformation. The Designer configures the
second set of source fields and master fields by default.
• Double-click the title bar of the Joiner transformation to open the Edit
Transformations dialog box.
• Select the Ports tab.
• Click any box in the M column to switch the master/detail relationship
for the sources. Change the master/detail relationship if necessary by
selecting the master source in the M column.
50. Creating a Joiner Transformation
Select the Condition tab and set the condition.
51. Configuring Joiner transformation
Joiner Setting Description
Case-Sensitive
If selected, the Informatica Server uses case-sensitive string
String
comparisons when performing joins on string columns.
Comparison
Specifies the directory used to cache master records and the index to
these records. By default, the caches are created in a directory
Cache Directory specified by the server variable $PMCacheDir. If you override the
directory, be sure there is enough disk space on the file system. The
directory can be a mapped or mounted drive.
Specifies the type of join: Normal, Master Outer, Detail Outer, or Full
Join Type
Outer.
52. Lookup Transformation
Used to look up data in a relational table, view, synonym or Flat
File.
It compares Lookup transformation port values to lookup table
column values based on the lookup condition.
Connected Lookups
Receives input values directly from another transformation in the
pipeline
For each input row, the Informatica Server queries the lookup table
or cache based on the lookup ports and the condition in the
transformation
Passes return values from the query to the next transformation
Un Connected Lookups
Receives input values from an expression using the
:LKP (:LKP.lookup_transformation_name (argument, argument,
...)) reference qualifier to call the lookup and returns one value.
With unconnected Lookups, you can pass multiple input values into
the transformation, but only one column of data out of the
transformation
53. Lookup Transformation
You can configure the Lookup transformation to perform different types of
lookups. You can configure the transformation to be connected or
unconnected, cached or uncached:
Connected or unconnected. Connected and unconnected transformations
receive input and send output in different ways.
Cached or uncached. Sometimes you can improve session performance by
caching the lookup table. If you cache the lookup table, you can choose to use
a dynamic or static cache. By default, the lookup cache remains static and does
not change during the session. With a dynamic cache, the Informatica Server
inserts rows into the cache during the session. Informatica recommends that
you cache the target table as the lookup. This enables you to look up values in
the target and insert them if they do not exist.
54. Diff bet Connected & Unconnected Lookup
Connected lookup Unconnected lookup
1) Receives input values directly from of a Receives input values from the result of
the pipe line transformation. LKP expression within other
transformation.
2) U can use a dynamic or static cache U can use a static cache.
3) Cache includes all lookup columns used Cache includes all lookup out put ports.
in the mapping.
4) Support user defined default values Does not support user defined default
values
55. Diff between Static & Dynamic Cache
Static Cache Dynamic Cache
1) U can not insert or update the U can insert rows into the cache as u pass
cache to the target
2) The Informatica Server does not The Informatica Server dynamically
update the cache while it processes inserts data into the lookup cache
the Lookup transformation and passes data to the target table.
56. Update Strategy Transformation
When you design your data warehouse, you need to decide what type of
information to store in targets. As part of your target table design, you
need to determine whether to maintain all the historic data or just the
most recent changes.
For example, you might have a target table, T_CUSTOMERS, that contains customer
data. When a customer address changes, you may want to save the original
address in the table, instead of updating that portion of the customer record. In
this case, you would create a new record containing the updated address, and
preserve the original record with the old customer address. This illustrates how you
might store historical information in a target table. However, if you want the
T_CUSTOMERS table to be a snapshot of current customer data, you would update
the existing customer record and lose the original address.
The model you choose constitutes your update strategy, how to handle changes to
existing records. In Power Mart and Power Center, you set your update strategy at
two different levels:
• Within a session. When you configure a session, you can instruct the
Informatica Server to either treat all records in the same way (for
example, treat all records as inserts), or use instructions coded into the
session mapping to flag records for different database operations.
• Within a mapping. Within a mapping, you use the Update Strategy
transformation to flag records for insert, delete, update, or reject .
57. Setting up Update Strategy at Session Level
During session configuration, you can select a single database operation
for all records. For the Treat Rows As setting, you have the following
options:
Setting Description
Treat all records as inserts. If inserting the record violates a primary or
Insert foreign key constraint in the database, the Informatica Server rejects the
record.
Treat all records as deletes. For each record, if the Informatica Server finds a
corresponding record in the target table (based on the primary key value),
Delete
the Informatica Server deletes it. Note that the primary key constraint must
exist in the target definition in the repository.
Treat all records as updates. For each record, the Informatica Server looks for
a matching primary key value in the target table. If it exists, the Informatica
Update
Server updates the record. Again, the primary key constraint must exist in
the target definition.
The Informatica Server follows instructions coded into Update Strategy
transformations within the session mapping to determine how to flag records
for insert, delete, update, or reject.
Data If the mapping for the session contains an Update Strategy transformation,
Driven this field is marked Data Driven by default.
If you do not choose Data Driven setting, the Informatica Server ignores all
Update Strategy transformations in the mapping.
58. Update Strategy Settings
setting you choose depends on your update strategy and the status of data in target tables:
Setting Use To
Populate the target tables for the first time, or maintaining a historical
Insert data warehouse. In the latter case, you must set this strategy for the
entire data warehouse, not just a select group of target tables.
Delete Clear target tables.
Update target tables. You might choose this setting whether your data
warehouse contains historical data or a snapshot. Later, when you
Update configure how to update individual target tables, you can determine
whether to insert updated records as new records or use the updated
information to modify existing records in the target.
Exert finer control over how you flag records for insert, delete, update,
or reject. Choose this setting if records destined for the same table
Data
need to be flagged on occasion for one operation (for example, update),
Driven
or for a different operation (for example, reject). In addition, this
setting provides the only way you can flag records for reject.