Datastage Tutorials-Datastage ETL Tool

Encode/Decode and Compress/Expand Stages

2015-08-20T10:40:00.001-07:00

Encode and Compress stages are processing stages that are used to encode/compress a data set using UNIX encoding command. Commands such as 'gzip' can be used. It converts an input data set from a sequence of records to a stream of raw binary data. The encoded data set is similar to an ordinary data set. Certain basic constraints are detailed below:

An Encoded or compressed data set cannot be used as an input to stages that perform column based processing . These can , however be used to pass data onto stages like copy.
The encoded or compressed data itself cannot be viewed,
The encoded or compressed data set cannot be re-partitioned.

Expland /Decode stage is used to decode/decompress the data that has been encoded/compressed by the Encode/compress is always a single stream, no meta data is required to be specified for either stage.

Remove Duplicate Stage Properties

2015-08-20T10:08:00.002-07:00

Key: Specifies the colun that defines duplicates. The key can be selected from the drop down key columns .

Duplicate to retain: Specifies which of the duplicate columns encountered to retain choose between first and last. It is set to first by default.

Following two properties are dependent properties for the key.

Sort as EBCDIC: To sort as in the EBCSIC Character set chose true.

Case Sensitive: Used to specify whether each key is case sensitive or not , this is set to true by default.

Tips:

The Property Duplicate to retain us useful when deciding the duplicates row to retain . For example if the input data is sorted on Empno and Dept no . In the remove duplicate stage we want to remove the duplicate records based on EMP NO , While from the duplicate records, we want to retain the row with max( Dept no) we should set this property to last, and vice versa.
The rejected /duplicate rows cannot be captured using this stage.
If the data is not sorted , you may get improper output from this stage.

Oracle Stage Output Properties

2015-08-19T09:40:00.002-07:00

Table: Name of the Oracle Table.
Write Method: The available write methods are:
Delete ( to delete rows in the target table)
Load ( to load the data in the target table)
Upset ( to perform an update & insert operation)

Write Mode: The available write modes are:

Append ( append new rows to the existing table)
Create ( Create the target tabel: this mode will fail if the table is already existing.)
Replace( Drop the table if exists and create the target table before loading)
Truncate ( delete the existing rows from the target table before pading)

DB Opetions: Allow to specify the connection option to the database.
Remote server : Alias of the tabase
Disable Constraints: If set to true , disable all necessary constraints before the load and enable them afterwards.
Index Mode: If set to Rebuild , skip index update during he load and rebuild them after the load is complete. Id set to maintenance, each partition will be loaded sequentially.

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Peek Stage Properties

2015-08-18T20:52:00.002-07:00

All Records ( After Skip) : Set to true to print all the records from each partition. By default it is set to face.

Number of records( Per partition) : Specifies number of records to print for each partition. The default is 10.

Period ( per partition) : Specifies the period after which a record from a partition is to be printed.

Ex: Setting period to P Prints the pth record from each partition.

Skip ( Per Partition) : Specifies the number of rows to skip from each partition . The default skip count is 0.

Peek All input Columns: Set to true to print all input column to specify only the selected columns, set to false.

Input Column to Peek: If the peek All input columns is set to false, then this property is used to specify a column to be printed. This property can be repeated to specify multiple columns.

All Partitions: Set to True by default . Set to false to specify that only certain partitions should have columns printed and specify which partition using partition number property.

Read more about What is Peek Stage in Datastage

Partition Number: If " All partition" is set to false then this property is used to to specify the partition for which the columns are to be printed. This property can be repeated to specify multiple columns.

Delimited String: Specified the string to be uses as a delimited on columns.

Peek Records Output mode: Specifies whether the output should go to an output column ( the peek records column) or to the job log.

Show Column names: Specifies whether the column name is to be printed in the output. Set to True, to print eh column name, followed by the colon, followed by column value, if false,the stage prints only the column value , followed by spaces.

Tip:

If the peeked records are output to a link instead of the job log, the link ordering tab on the stage page should be used to choose the output link for the peeked records. By default , peeked records are written to the last link added.

The peek stage is a development /debug stage . It can have a single input link and any number of output links. The peek stage us a development lets you print record column values either to the job log or to a separate output link as the stage copies records from its input data set . To one or more output data sets, the peek stage can be helpful for monitoring the progress of application or to diagnose a bug in application.

What is Peek Stage in Datastage ?

2015-08-18T11:50:00.002-07:00

The peek stage is a development/debug stage. It can have a single input link and any number of output links. The peek stage can be used to print the record column values to the job log or to a separate output link as it copies records from its input dataset to one or more output datasets. It can be used to read columns for a fixed number of records from each partition or all records across partitions.

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SAS Parallel Data Set Stage

2015-08-18T08:11:00.000-07:00

The SAS Parallel Data Set Stage is a file stage. It allows reading data from or writing data to a parallel SAS data set in Conjunction with an SAS Stage. The stage can have a single input link or a single output link . It can be configured o execute parallel or sequential mode. Datastage uses an SAS Parallel data set to store data being operated on by an SAS Stage in a persistent form. An SAS Parallel data set is a set of one or more sequential SAS Data sets, with a header file specifying the names and locations of all the component files. By Convention, the header file has the suffix .psds.

Topologies Supported in Datastage

2015-08-17T20:52:00.003-07:00

Datastage Parallel Extender is built on a highly scalable parallel software architecture that delivers high levels of through =out and performance. For maximum scalability, integration spftware must do more than run on symmetric multiprocessing ( SMP) and Massively parallel processing (MPP)Computer systems .If the data integration platform does not saturate all of the nodes of the MPP Box or system in the cluster or Grid, Scalability cannot be maximized . The Datastage parallel Extenders components fully exploit SMP, Clustered, Grid and MPP Environments to optimize the use of all available hardware resources. Datastage Parallel Extenders supports multiple topologies to satisfy a variety of your data integration and hardware business requirements as follows.

Two Tier
Three Tier
Cluster
Grid

What is the Datastage Expression Editor

2015-08-13T10:47:00.001-07:00

Expression Editor helps you to enter the correct expressions when you edit the transformer stages.

a) Facilitate the entry of the expression elements.

b) Compare the names of the frequently used variables.

c) Validate the expression.

The Expression Editor can be opened from :

a) Output link Derivation Cells.

b) Stage Variable Derivation cells.

c) Constraint dialog box

Whenever the insertion point s in an expression box, use the Expression Editor to suggest the next element in your expression. Do this by right clicking the box , or by the clicking suggest button to the right of the box. This opens the suggest operand or suggest operator menu.

Which Describes the default behaviour when you create a table in SQL ?

2015-08-13T08:04:00.000-07:00

The table is accessible to all users.
Tables are created in the public Schema.
Tables are created in your schema.
Tables are created in the DBA schema.
You must specify the schema when the table us created.

What is Datastage Keep File Partitions

2015-08-13T05:58:00.000-07:00

Set True to Partition the read data set according to the organization of the input File. Usually, setting "True" allows better performance in subsequent stages. However, it is not desirable if the input file already has sort in a certain order.

Datastage Real time scenario Transformer Stage Question

2015-08-12T23:10:00.000-07:00

If I have a Source like below

a,b,c,1,2,3 ( All of them are in 1 column )

If I want to get output as below

a,b,c,1 ( In 1st row )

a,b,c,2 ( In 2nd row )

a,b,c,3 ( In 3rd row )

Solution:

If you have Source as below

a,b,c,1,2,3 ( All this in 1 column)

Sequential Stage file ---->Transformer Stage ---->( 3 Out put links)---> Funnel Stage ---->Outout file

In Transformer Stage - as below

O/P Link1 - Col1,Col2,COl3,Col4
O/P Link2 - Col1,Col2,COl3,Col4
O/P Link3 - Col1,Col2,COl3,Col4

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What is Missing file Mode in Datastage

2015-08-12T20:48:00.002-07:00

Specific the action to be taken if the file is missed.

a) Depend ( the default is Error unless the file name has a node name prefix of *, in which case it is OK)

b) Error ( to stop job is one of the files mentioned does not exist.

c) OK ( to skip the file and continue the process)

Error to stop job if one of the files mentioned does not exist: OK to skip it. Depends means the default is Error unless the file name has a node name prefix of *, in which case it is OK.

Standards of Datastage

2015-08-12T10:04:00.002-07:00

It is important to establish and follow consistent standards in:

1) Directory Structures for installation and application support directories.

2) Naming Conventions, especially for Datastage project categories stage, names and links.

3) All DataStage Jobs should be documented with the short description field as well as Annotation fields.

4) It is the datastage developers responsibility to make personal backups of their work on their local workstation using Datastage's DSX export capability . This can also be used for integration with source code control systems.

Overview:Best Practices of Datastage

2015-08-12T10:00:00.000-07:00

This Section Provides an Overview of recommendations for standard practices.

The recommendations are categorized as follows.

a) Standards

b) Development Guidelines

c) Component Usage

d) Datastage Data Types

e) Partitioning Data

f) Collecting Data

g) Sorting

h) Stage Specific Guidelines.

All About XML Output Stage

2015-08-12T09:49:00.003-07:00

The XML Output Stage is used to convert tabular data such as tables and sequential files to XML hierarchical structures. XML Output stage supports single input link and zero or more output links. The XML Output stage required X-Path expressions to transform tabular data to XML. If a stage has both input and output links. X-Path expressions are required on both links.

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All About Generic Stage in Datastage

2015-08-12T06:38:00.000-07:00

The Generic Stage is a Processing Stage. It has any number of input links and any number of Outpu links. The generic Stage allows you to call on Orchestrate Operator from with in a Datastage Stage and Pass it options a required.

Change Apply Stage

2015-08-11T09:18:00.000-07:00

The change Apply Stage is a Processing stage. It has two input links and one output link. The two input links are the "change" and "Before" data set. The " Change" Data set is generated using the " Before" and " After " data sets, that are in turn taken as input from the change capture stage. The " Before" data set is a replica of the before data set in the change capture stage. Comparisons are done between the change data set and before data set on the key column. If the Before keys come before the change keys in the specified sort order, then the before record is copied to the output . If the before keys are equal to the change keys, the behavior depends on the code in the " change code" column of the change record as detailed below.

Insert : The change record is copied to the output . The stage retains the same before record for the next comparison.

Delete: If the value columns are the same or if the check value columns on delete are specified as false, the change and before records are both discarded no record is transferred to the output. If the value columns are not the same, the before record is copied to the ourput and the stage retains the same change record for the next comparison.

Edit: The change record is copied to the output: the before record is discarded.

Copy: The change record is discarded : the before record is copied to the output.

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Aggregation Method

2015-04-19T02:13:00.001-07:00

Aggregator stage is a transforming stage in datastage is used for making grouping and synopsis operations.By Default Aggregator stage will execute in parallel mode in parallel occupations.

Note : In a Parallel domain ,the way that we divideinformation before making group and synopsis(summary) will influence the results. If you divideinformation by utilizing round-robin technique and afterward records with same key qualities will distribute crosswise over distinctive partitions and that will give in right results.

Aggregator stage has two diverse aggregation Methods.

1) Hash : Use hash mode for a moderately little number of gatherings; eventually, less than 1000 groups after every megabyte of memory.

2) Sort : Sortmode obliges the input information set to have been parcel sorted with the greater part of the gathering keys determined as hashing and sorting keys.Unlike the Hash Aggregator, the Sort Aggregator requires presorted information, yet just keeps up the calculations for the latestgroup in memory.

Aggregation Data Type:

Eventually, aggregator stage computation output section is a double information type and ifyou need decimal output then include below given property as demonstrated.

If you are only utilizing single key section for grouping keys then there is no compelling reason to sort or hash parcel the approaching information.

Aggregator and filter stage with example

If we have a data as below given :

table_a
dno,name
10,siva
10,ram
10,sam
20,tom
30,emy
20,tiny
40,remo

& we have to get the same different times records into the one target.
Then, one records not repeated with respected (regarded) to dno need to go to another target.

Take Job design as

Read and load the information in sequential file.

In Aggregator stage choose group =dno

Aggregator type = count rows

Count output column =dno_cpunt( user defined )

In output Drag and Drop the columns required.Than click ok

In Filter Stage

----- At first where clause dno_count>1
-----Output link =0
-----At second where clause dno_count<=1 -----output link=0 Drag and drop the outputs to the two targets. Give Target file names and Compile and Run the JOb. You will get the required data to the Targets.

Aggregator stage to find number of people group-wise

We can use Aggregator stage to find number of people each in each department.

For example, if we have the data as below

e_id,e_name,dept_no
1,sam,10
2,tom,20
3,pinky,10
4,lin,20
5,jim,10
6,emy,30
7,pom,10
8,jem,20
9,vin,30
10,den,20

Take Job Design as below

Seq.-------Agg.Stage--------Seq.File

Read and load the data in source file.

Go to Aggregator Stage and Select Group as Dept_No

and Aggregator type = Count Rows

Count Output Column = Count ( This is User Determined)

Click Ok ( Give File name at the target as your wish )

Compile and Run Job

Aggregator stage with real time scenario example

Aggregator stage works on groups.
It is used for the calculations and counting.
It supports 1 Input and 1 Outout

Example for Aggregator stage

Input Table to Read

e_id, e_name, e_job,e_sal,deptno

100,sam,clerck,2000,10
200,tom,salesman,1200,20
300,lin,driver,1600,20
400,tim,manager,2500,10
500,zim,pa,2200,10
600,eli,clerck,2300,20

Now, our requirement is to discover the max. salary from every department number.
As per sample information, we only have two dept. here.

Take Sequential File for reading the info and take Aggregator for the calculations.

& Take sequential file to load in to the target.

That’s we can take like this

Seq.File--------Aggregator-----------Seq.File

Read data in Sequential File

& in Aggregator Stage ---In Properties---- Select Group =Dept. No.

Choose e_sal in Column for calculations

that is due to calculations of maximum salary based on entire dept. Group.

Choose output file name in 2nd sequential file.

Here compile & run.

It will work fine.

Acquiring Datastage Tutorial For Complete Knowledge On The Tool

2015-04-09T06:43:00.006-07:00

With the tremendous advancement in technology, Datastage has become a common tool for designing, developing and running applications in a data warehouse. In fact, it can also be considered as an ETL Tool that is used to extract, transform and load the data into data warehousing. The data is then used to generate reports and help in decision making processes. They also help in the processes of data cleansing, profiling and extracting. Hence, the demand for Datastage or IBM Information server is huge. Acquiring knowledge on the tool will definitely help you to a great extent.

Well, you will be happy to know that we are one of the leading companies offering Datastage Tutorial. We offer complete guidance to the candidates interested in learning different processes associated with the same. We start right from the basic concepts of Datastage so that candidates acquire knowledge on the usage of the tool. Some of the common concepts that we teach include compiler, data modelling, data warehousing and the different versions of Datastage. In addition to that, we also teach about the errors, the features, the partitioning concepts and project concepts. With us, you will learn about the roles and responsibilities of a developer working with this particular tool and the special characters surrounding the tool.

Our Datastage Tutorial also includes examples so that it is easy for developers to understand the concept and the exact processes of its applications. At different stages of our course, we will offer Datastage examples of different phases. Right from the filter stage to the funnel stage, generators stage, merge stage and lookup stage, we will provide complete examples making it easier for our learners to get hands on experience. Datastage can extract data from any source and load it into any target. It also runs on any platform and data can be easily distributed across the nodes with different partition techniques.

We also provide our candidates with hosts of interview questions on this concept so that they are thoroughly prepared and confident during any interview. Our experienced and qualified professionals will take special care in guiding you and making sure that you are thoroughly clear on the subject. The Datastage interviewquestions that we prepare are some of the most updated and common questions asked by different companies while recruiting Datastage candidates. Therefore, you will reap benefits with the questions that you come across in our tutorial and can boost your self-confidence in using the tool.

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All about Datastage Director

2015-04-02T20:52:00.001-07:00

Datastage Director is a client component that validates, runs , schedules and monitors jobs that are run by the Datastage Server. Datastage Director is accessed through a separate icon like DS Designer . It can also access from with DS Designer or DS manager. by selecting jobs can be " Reset' and Run from DS Director .

Select the Reset button or the RUN. The current status of a DS Job can be monitored from the status view or can see the Job logs of the Director . The compiled Jobs can be scheduled . These options are activated only when a job is selected.

In Datastage Director,

Run/Validate a colbiled Job
Stop a Running Job
Reset an aborted Job
Schedule Jobs/Batches
Create a New Batch
View Staus / Monitor Jobs
View / Purge Job Log Entries
Clean Up Resources
Message Handling

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Sequencer Palette Stages Can be grouped into four Categories

2015-04-01T20:01:00.000-07:00

Job Sequence Stages:

1) Run :
a) Job Activity: Run a Job( ASequence Job Can ve called as well )
b) Executive Command : Run a System Command ( OS Command or a Script)
c) Notification Activity : Send an email ( SMPT Server needed)

2) Flow Control
a) Sequencer: Make a any /all Decision.
b) Wait for File: Go When file exists /doesn't exists.
c) Start loop/End loops : Construct Loops
d) Nested Condition : Implement complex control structures.

3) Error Handling:
a) Exception Handler.
b) Terminator: Send Stop Signal to the calling sequence

4) User Variables and Routine Activity
a) User Variables can be created to use downstream .
b) Routines can be called using Routine Activity.

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Role of merge stage in Datastage

2015-04-01T07:21:00.003-07:00

The Merge stage is just a processing stage. It can have any number of data connections or input links, a solitary output link and the same number of reject links as there are update input links.(as like to DS documentation)

Merge stage consolidates a master dataset with one or more overhaul datasets in light of the key columns. The output record contains all the segments from expert record in addition to any extra sections from every upgrade record that are needed.

An expert record and upgrade record will be blended just if both have same key segment values. The data sets input to the Merge stage should be key partitioned and sorted. This makes sure those columns with the same key segment qualities are placed in the same parcel and will be handled by the same hub. It likewise minimizes memory prerequisites in light of the fact that less lines need to be in memory at any one time.

As a component of preprocessing your information for the Merge stage, you ought to additionally expel copy records from the expert information set. If you have more than one upgrade data set, you must expel copy records from the redesign information sets too.

Not at all like Join stages and Lookup stages, the Merge stage permits you to determine a few reject joins. You can route update link rows that neglect to match an expert column down a reject interface that is particular for that link. You should have the same number of rejected linksas you have upgrade joins. The Link Ordering tab on the Stage page gives you a chance to indicate that update links send rejected columns to those rejected links. You can likewise indicate whether to drop unmatched expert columns or output them on the output data link.

Example :

Master dataset:
CUSTOMER_ID	CUSTOMER_NAME
1	UMA
2	POOJITHA

Update dataset1
CUSTOMER_ID	CITY	ZIP_CODE	SEX
1	CYPRESS	90630	M
2	CYPRESS	90630	F

Output:
CUSTOMER_ID	CUSTOMER_NAME	CITY	ZIP_CODE	SEX
1	UMA	CYPRESS	90630	M
2	POOJITHA	CYPRESS	90630	F

Merge stage configuration steps :

Unmatched Masters Mode: Keep implies that unmatched columns (those with no upgrades) from the expert links are output; Drop implies that unmatched lines are dropped.

Caution on reject Updates: True to create a notice when awful records from any update links are rejected.

Caution on unmatched masters:True to create a notice when there are unmatched columnsfrom the expert connection.

Partitioning: Hash on both expert input and update input include as demonstrated as follows

Aggregate and run the job:

Situation 1

Expel a record from the updates 1 and check the output:

Check for the datastage cautioning in the job log as we have chosen. Warn on unmatched experts = TRUE

stg_merge,0: Master record (0) has not updates.

stg_merge,1: Update record (1) of dataset 1 is dropped; no mastersare cleared out.

Situations 2

Drop unmatched expert record and catch reject records from updates 1.

Situation 3

Insert a copy record with same client id in the expert dataset and check for the outcomes.

Take a gander at the outputand it is clear that merge stage consequently dropped the copy record from expert dataset.

Situation 4:

Added new update dataset 2 that contains taking after data.

Update Dataset2
CUSTOMER_ID	CITIZENSHIP
1	INDIAN
2	AMERICAN

Still, we have copy row in the expert dataset. If you accumulate the job with above configuration, you will get gathering blunder like below. If you view ate the above figure you can see 2 lines in the outputbecause we have a coordinating column for the customer_id = 2 in the updates 2 .

Situation 5

Add a copy linefor customer_id=1 in updates dataset. Now, we have copy record both in expert dataset and updates1. Run the job and check the outcomes and warningsin the occupation log.

No change in the outcomes and merge stage naturally dropped the copy column.

Situation 6

Modify a copy line for customer_id=1 in updates1 dataset with zipcode as 90630 rather than 90620.

Run the employment and check output results.

We can ran the same job numerous times and discover the merge stage is taking first record nearing as information from the updates 1 and dropping the following records with same client id.

This post covered the vast majority of the merge situation.

How lookup stage works in Datastage ?

2015-03-31T07:16:00.000-07:00

Like Join and Merge stage, Lookup stage has various multiple input links, one is basic and others are reference, as per thatlookup operation takes place. But it does nothave condition like Merge stage i.e. 'Reject Links' ought to be equivalent to overhaul data interfaces likewise it not oblige information on any of the input links to be sorted. Lookup stage gives four conditions rely upon that in coming days of output data depends.
We will see these conditions in 'Step 4'.

Presently, let’s attempt to actualize or implement Lookupstage with the assistanceof below given tables.

Table 1

ID	First Name	Last Name	Location	Network ID	EmailID
1	Jach	Simmons	Chicago	JS524	Letsc@gmail.com
2	Shumas	Jane	LA	Sj145	Jaene@ymail.com
3	Jonty	Waughn	Sydney	JW927	JontyW@sdbh.com
4	Suhana	Safar	Maxico	SS99	Sas@gmail.com

Table 2

ID	Dept	Dept Head
1	Electronics	Paul
3	CS	Jack
4	TS	Summur
5	IT	Sean

Table 3

ID	Training Cent
1	CKG
2	AMD
3	WC

Step 1 : Design a job structure like underneath.

Consider Employee table as Primary connection or link as demonstrated. Contingent on every or each record in Primary connection, Lookup Stage performs turn upward operation on Reference Link according with key section.

Consider Employee's department information that is table secondand Employee's training focus that is Table 3 as data on two reference links.

Step 2 : Now, we are going to Lookup Stage (Named as lkp_emp_det in configuration). Twofold click on Lookup Stage. Taking after window will pop up. Left sheet is for all inputs and right sheet is for output. Initially, link detail table is for Primary link. Second and third are for reference links.

Request of these reference linkscan be changed by utilizing this image on Title Bar as shown.

Step 3 : In left sheet, guide Key segment (here 'ID') by simply dragging it to the particular key section in reference joins. Guide all staying obliged segment to right sheet as demonstrated.

Step 4 : One of the most vital step is to situated gaze upward conditions that we can do by utilizing second choice on Title bar. Simply click on it, taking after window will pop up.

There is a rundown of reference connections in 'Connection Name' section. In "Condition" section,we can give conditions for every reference link. Whether this condition won't meet then what will happen to that information is chosen by 'Condition Not Met' section and if lookup comes up short it is chosen by 'Lookup Failure' segment.

Proceed with : Data will be sent to the Output link.

Reject : Data will be sent to the Reject link.

Drop : Data will neither go to Output link nor to Reject link.

Fail : Job will falls fail.

In this case, how about we first strive for without condition in "Condition" segment and "Proceed with" and "Reject" in different sections.

Step 5 : Compile and run the JOB.

How about we see what the outputis :

Output : Stream link

It's demonstrating two records. As we have given 'Lookup Failure' condition as 'Reject', those records from essential connection that are not coordinated with reference join information are gatheredin Reject Link "rjct_primary_rec" as demonstrated as follows.

Step 6 : Let's attempt to design for "Condition" section in 'Lookup Stage Conditions' sheet.

Recently put condition as ID=3 and "Reject" under 'Condition Not Met' as demonstrated as follows.

But ID=3 all records will get dismisses and get put away in 'Reject join'. Here information for ID=2 get rejected and we will get output for Stream interface as indicated.

Output for 'Reject Link'

Note : Reject Link shows rejected record from basic info link just.

Practice for "Drop" and "Come up short" or fail condition.

Conclusion

DataStagehas constantly performed joins proficiently when there are accurate key fields that match utilizing the lookup, join or union stage. Range lookups are all the more difficult as its a less effective approach to join whether you are destroying it an ETL work or on a database. You can do an extent lookup in DataStage 7 utilizing a lookup stage and a channel stage, you can do it utilizing a meager lookup and you can do it by stacking both tables into a database organizing territory and going along with them in SQL. This exercise demonstrates to destroy it a solitary Lookup stage giving a much less complex outline.

Get to know the facts of Join Stage in Datastage

2015-03-30T04:18:00.000-07:00

The "Join" stage is a processing stage that performs a join operation on two or more input information sets and afterward gives output as one resultant data set.

There are Four sorts of joins in DataStage:

1. Left Outer Join: It joins two or more tables on JOIN condition where result table preserves record from first (Left) table and puts NULL wherever unmatched record from second (Right) table.

2. Right Outer Join: It is precisely inverse to the Left external join. It joins two tables on JOIN condition where result table preserves record from first (Right) table and puts NULL wherever unmatched record from second (Right) table.

3. Inner Join: It joins two or more tables and returns just those records that fulfill join condition.

4. Full Outer Join: It joins two or more tables and returns both coordinated and unmatched records from all tables.

Initially, let’s attempt to perform JOIN on two tables in DataStage.

Table 1:

ID	First Name	Last Name	Location	Network ID	EmailID
1	Jach	Simmons	Chicago	JS524	Letsc@gmail.com
2	Shumas	Jane	LA	Sj145	Jaene@ymail.com
3	Jonty	Waughn	Sydney	JW927	JontyW@sdbh.com
4	Suhana	Safar	Maxico	SS99	Sas@gmail.com

Table 2

ID	Dept	Dept Head
1	Electronics	Paul
3	CS	Jack
4	TS	Summur
5	IT	Sean

Step 1 : Design your job like below.

Step 2 : Configure Table 1 and Table 2 in Sequential record 1 and Sequential document 2.

Step 3 : While designing section name, verify that you ought to give right Datatype against every segment overall output won't be appropriately sort. Give "Number" Datatype for "ID" where we can utilize "varchar" too for it.

Step 4 : Double click on the JOIN stage. Under Properties tab notice 'Key = ID'. Beneath this tab you can notice Join Type as Inner, Left outer or Right Outer. Now, we should Practice for Left outer Join first.

Step 5 : Beneath'link order' tab you can say or mention Left and right link on that Type of join is depends. i.e.Left or Right external. Order of link doesn't make a difference for Inner Join.

Step 6 : Map the obliged section under output stage.

Step 7 : Link order is depends on key section need to mapped. Those are not mapped will be shown by Red shading or it will indicate lapse while accumulating.

Step 8 : Compile and Run

Output:

1. Left external Join

Left link = lnk_emp_det Right link = lnk_dept_det said under 'Connection or link

As we already said that Table 1 as left table, result table demonstrates all records from left table with coordinated records from right i.e. Table 2. Unmatched records are vacant as indicated previously

2. Right Outer Join

Before accumulate to job for Right outer join, verify that you have mapped outputaccurately.

Key section from output ought to be mapped from Right table. Mention beneath:

Output

Precisely inverse to Left Outer Join, result Table demonstrates all records from Right table i.e Table 2 and unmatched records are void.

3. Internal Join:

While mapping for output section in 'Inward Join', Key segment (here Key=ID) ought to be mapped from Left table as examined in Step 6 else it will toss mistake amid compilation.Result table shows just coordinated record from both the tables.

4. Full Outer Join:

For Full Outer Join, we can guide key section from both table as demonstrated. It demonstrates an odd passage for key segment which is not exhibit. Here, its shown by "0" in yield

Conclusion

The join stage supports two or more sorted information connections or links and one output link The join stage proofreader or editor permits you to determine the keys on which join is performed. More than one key can be determined. Determined keys ought to have same name on all connections. No fall flat/dismiss alternative for missed matches. Link requesting is critical while utilizing left or right external join furthermore the input information on all connections to join stage ought to be sorted.

The Join stage does not give reject link handling to unmatched records. Whether unmatched columns ought be captured, and an external join operation should be performed so that when a match does not happen, the Join stage embeds Null worth into the unmatched non-scratch segments gave non-scratch segment is characterized as null-able on the Join info links. After Join Stage, Use Transformer to channel Null records with the assistance of is Null Built capacity.

Hope this article is useful is useful for you.

Read the explanations of transformer stage in Datastage

2015-03-28T21:57:00.001-07:00

Transformer is a standout among-st the most capable stages in datastage. This comes under the Processing category in the palette. Transformer stage is helpful to do the change needed according to specific aspect.

This stage neither extractsnor compose data to a target database. It handles the extracted and performs a few business change and control the information stream. There are many parameters, functions and schedules to help for obliged changes.

Let’s begin with the Functions given by Transformer first:

String Function
Consistent Functions
Date & Time Functions
Numerical Functions
Null Handling Functions
Number Functions
Crude Functions
Vector Function
TypeConversion Functions
Utility Functions

Why we need Transformer stage ?

BasicTransformer is identified with Datastage Server just Features are only normal. A few circumstances are much in Transformer stage which is accessible in datastageparallel extender like functionsand calling different stages.

Transformer stages can have a basic input link, multiple reference input links and multiple output links. The link from the principle data input source is assigned as the primary input link. You can indicate an operation by entering a expression or selecting a change to apply to the particular data. You can likewise characterize custom change functions that are restore in the store for reuse.

Disadvantages of transformer stage in datastage ?

Eventually, all stages (filter,surrogate, row, column generator etc....except transform stage) are run in orchistation scripting dialect (OSH). But Transformation stage will run on C++ code.If the job to havetransformation stage then that job will run in OSH code and C++ code. C++ code is not a part of in datastage. Then, job will take of much time to operate. This is the only the draw back in Transformer stage in data stage.

Transformer stages could have any number of input links and output links. Input links can be basicor reference (utilized for lookups) and there must be single primary data and any number of reference inputs.

Kindly allude to the examples underneath to figure out what is the utilization of transformers. In job design portrayed beneath there is a regular job stream actualized. The job is utilized to loadclients into the datawarehouse. The information is extricated from an ODBC information source then filtered, accepted and refined in the first transformer. Rejected (not approved) records are logged into a successive document. The second transformer performs a lookup(into a nation dictionaryhash record) and does a fewother information mappings. The information is loaded into an Oracle database.

Create a design of typical datastagework with acceptances and mapping
Create a basic transformer design
Make design of sample transformer with data channel and reject flow

Conclusion

The Transformer Stage is a dynamic Stage that can have a solitary input link and multiple output links. It is an extremely robustStage along with lots of inbuilt functionality. Transformer Stage dependably creates C-codethat is then accumulated to a parallel part. So the overheads for utilizing a transformer Stage are high. In any work, it is basic that the utilization of a transformer is kept to a base and rather different Stages are used like Copy Stage can be utilized for mapping input links with multiple output links with no changes. Channel Stage can be utilized for separating out information in light of certain criteria. Switch Stage can be utilized to guide one input link with multiple output links in light of the estimation of a selector field. It is additionally advisable to decrease the quantity of transformers inthe job, by joining the rationale into a solitary transformer as opposed to having numerous transformers .

Read the next post to know the more information about Datastage.