Friday, October 23, 2015

Compile a Datastage job/multiple jobs through Command Line

Question

How do you compile all jobs in a project via command line?

Answer

Perform the following:
  1. Open a DOS command prompt on the client
  2. Run the following from C:\IBM\InformationServer\Clients\Classic:

    dscc.exe /H hostname /U username /P password project_name /J jobname

    Note: The job name can be * to compile all the jobs in the Project.

Note: you cannot use this command to compile a single job.

Thursday, October 22, 2015

Datastage All Stages

Datastage parallel stages groups

DataStage and QualityStage stages are grouped into the following logical sections: 
  • General objects
  • Data Quality Stages
  • Database connectors
  • Development and Debug stages
  • File stages
  • Processing stages
  • Real Time stages
  • Restructure Stages
  • Sequence activities
Please refer to the list below for a description of the stages used in DataStage and QualityStage. 
We classified all stages in order of importancy and frequency of use in real-life deployments (and also on certification exams). Also, the most widely used stages are marked bold or there is a link to a subpage available with a detailed description with examples. 

DataStage and QualityStage parallel stages and activities

General stages

General elements





  • Link indicates a flow of the data. There are three main types of links in Datastage: stream, reference and lookup.

    • Container (can be private or shared) - the main outcome of having containers is to simplify visually a complex datastage job design and keep the design easy to understand.
    • Annotation is used for adding floating datastage job notes and descriptions on a job canvas. Annotations provide a great way to document the ETL process and help understand what a given job does.
    • Description Annotation shows the contents of a job description field. One description annotation is allowed in a datastage job.


    Debug and development stages

    Debug and development stages

    • Row generator produces a set of test data which fits the specified metadata (can be random or cycled through a specified list of values). Useful for testing and development. Click here for more..
    • Column generator adds one or more column to the incoming flow and generates test data for this column.
    • Peek stage prints record column values to the job log which can be viewed in Director. It can have a single input link and multiple output links.Click here for more..
    • Sample stage samples an input data set. Operates in two modes: percent mode and period mode.
    • Head selects the first N rows from each partition of an input data set and copies them to an output data set.
    • Tail is similiar to the Head stage. It select the last N rows from each partition.
    • Write Range Map writes a data set in a form usable by the range partitioning method.
    Processing stages

    Processing stages

    • Aggregator joins data vertically by grouping incoming data stream and calculating summaries (sum, count, min, max, variance, etc.) for each group. The data can be grouped using two methods: hash table or pre-sort. Click herefor more..
    • Copy - copies input data (a single stream) to one or more output data flows
    • FTP stage uses FTP protocol to transfer data to a remote machine
    • Filter filters out records that do not meet specified requirements.Click here for more..
    • Funnel combines mulitple streams into one. Click here for more..
    • Join combines two or more inputs according to values of a key column(s). Similiar concept to relational DBMS SQL join (ability to perform inner, left, right and full outer joins). Can have 1 left and multiple right inputs (all need to be sorted) and produces single output stream (no reject link). Click here for more..
    • Lookup combines two or more inputs according to values of a key column(s). Lookup stage can have 1 source and multiple lookup tables. Records don't need to be sorted and produces single output stream and a reject link. Clickhere for more..
    • Merge combines one master input with multiple update inputs according to values of a key column(s). All inputs need to be sorted and unmatched secondary entries can be captured in multiple reject links. Click here for more..
    • Modify stage alters the record schema of its input dataset. Useful for renaming columns, non-default data type conversions and null handling
    • Remove duplicates stage needs a single sorted data set as input. It removes all duplicate records according to a specification and writes to a single output
    • Slowly Changing Dimension automates the process of updating dimension tables, where the data changes in time. It supports SCD type 1 and SCD type 2.Click here for more..
    • Sort sorts input columns.Click here for more..
    • Transformer stage handles extracted data, performs data validation, conversions and lookups.Click here for more..
    • Change Capture - captures before and after state of two input data sets and outputs a single data set whose records represent the changes made.
    • Change Apply - applies the change operations to a before data set to compute an after data set. It gets data from a Change Capture stage
    • Difference stage performs a record-by-record comparison of two input data sets and outputs a single data set whose records represent the difference between them. Similiar to Change Capture stage.
    • Checksum - generates checksum from the specified columns in a row and adds it to the stream. Used to determine if there are differencies between records.
    • Compare performs a column-by-column comparison of records in two presorted input data sets. It can have two input links and one output link.
    • Encode encodes data with an encoding command, such as gzip.
    • Decode decodes a data set previously encoded with the Encode Stage.
    • External Filter permits speicifying an operating system command that acts as a filter on the processed data
    • Generic stage allows users to call an OSH operator from within DataStage stage with options as required.
    • Pivot Enterprise is used for horizontal pivoting. It maps multiple columns in an input row to a single column in multiple output rows. Pivoting data results in obtaining a dataset with fewer number of columns but more rows.
    • Surrogate Key Generator generates surrogate key for a column and manages the key source.
    • Switch stage assigns each input row to an output link based on the value of a selector field. Provides a similiar concept to the switch statement in most programming languages.
    • Compress - packs a data set using a GZIP utility (or compress command on LINUX/UNIX)
    • Expand extracts a previously compressed data set back into raw binary data.
    Database stages

    File stage types

    • Sequential file is used to read data from or write data to one or more flat (sequential) files.Click here for more..
    • Data Set stage allows users to read data from or write data to a dataset. Datasets are operating system files, each of which has a control file (.ds extension by default) and one or more data files (unreadable by other applications). Click here for more info
    • File Set stage allows users to read data from or write data to a fileset. Filesets are operating system files, each of which has a control file (.fs extension) and data files. Unlike datasets, filesets preserve formatting and are readable by other applications.
    • Complex flat file allows reading from complex file structures on a mainframe machine, such as MVS data sets, header and trailer structured files, files that contain multiple record types, QSAM and VSAM files.Click here for more info.
    • External Source - permits reading data that is output from multiple source programs.
    • External Target - permits writing data to one or more programs.
    • Lookup File Set is similiar to FileSet stage. It is a partitioned hashed file which can be used for lookups.
    Database stages

    Database stages


    • Oracle Enterprise allows reading data from and writing data to an Oracle database (database version from 9.x to 10g are supported).
    • ODBC Enterprise permits reading data from and writing data to a database defined as an ODBC source. In most cases it is used for processing data from or to Microsoft Access databases and Microsoft Excel spreadsheets.
    • DB2/UDB Enterprise permits reading data from and writing data to a DB2 database.
    • Teradata permits reading data from and writing data to a Teradata data warehouse. Three Teradata stages are available: Teradata connector, Teradata Enterprise and Teradata Multiload
    • SQLServer Enterprise permits reading data from and writing data to Microsoft SQLl Server 2005 amd 2008 database.
    • Sybase permits reading data from and writing data to Sybase databases.
    • Stored procedure stage supports Oracle, DB2, Sybase, Teradata and Microsoft SQL Server. The Stored Procedure stage can be used as a source (returns a rowset), as a target (pass a row to a stored procedure to write) or a transform (to invoke procedure processing within the database).
    • MS OLEDB helps retrieve information from any type of information repository, such as a relational source, an ISAM file, a personal database, or a spreadsheet.
    • Dynamic Relational Stage (Dynamic DBMS, DRS stage) is used for reading from or writing to a number of different supported relational DB engines using native interfaces, such as Oracle, Microsoft SQL Server, DB2, Informix and Sybase.
    • Informix (CLI or Load)
    • DB2 UDB (API or Load)
    • Classic federation
    • RedBrick Load
    • Netezza Enterpise
    • iWay Enterprise
    Real time

    Real Time stages

    • XML Input stage makes it possible to transform hierarchical XML data to flat relational data sets
    • XML Output writes tabular data (relational tables, sequential files or any datastage data streams) to XML structures
    • XML Transformer converts XML documents using an XSLT stylesheet
    • Websphere MQ stages provide a collection of connectivity options to access IBM WebSphere MQ enterprise messaging systems. There are two MQ stage types available in DataStage and QualityStage: WebSphere MQ connector and WebSphere MQ plug-in stage.
    • Web services client
    • Web services transformer
    • Java client stage can be used as a source stage, as a target and as a lookup. The java package consists of three public classes: com.ascentialsoftware.jds.Column, com.ascentialsoftware.jds.Row, com.ascentialsoftware.jds.Stage
    • Java transformer stage supports three links: input, output and reject.
    • WISD Input - Information Services Input stage
    • WISD Output - Information Services Output stage
    Real time

    Restructure stages

    • Column export stage exports data from a number of columns of different data types into a single column of data type ustring, string, or binary. It can have one input link, one output link and a rejects link. Click here for more..
    • Column import complementary to the Column Export stage. Typically used to divide data arriving in a single column into multiple columns.
    • Combine records stage combines rows which have identical keys, into vectors of subrecords.
    • Make subrecord combines specified input vectors into a vector of subrecords whose columns have the same names and data types as the original vectors.
    • Make vector joins specified input columns into a vector of columns
    • Promote subrecord - promotes input subrecord columns to top-level columns
    • Split subrecord - separates an input subrecord field into a set of top-level vector columns
    • Split vector promotes the elements of a fixed-length vector to a set of top-level columns
    Data quality QualityStage

    Data quality QualityStage stages

    • Investigate stage analyzes data content of specified columns of each record from the source file. Provides character and word investigation methods.
    • Match frequency stage takes input from a file, database or processing stages and generates a frequence distribution report.
    • MNS - multinational address standarization.
    • QualityStage Legacy
    • Reference Match
    • Standarize
    • Survive
    • Unduplicate Match
    • WAVES - worldwide address verification and enhancement system.


    Sequence activities

    Sequence activity stage types

    • Job Activity specifies a Datastage server or parallel job to execute.
    • Notification Activity - used for sending emails to user defined recipients from within Datastage
    • Sequencer used for synchronization of a control flow of multiple activities in a job sequence.
    • Terminator Activity permits shutting down the whole sequence once a certain situation occurs.
    • Wait for file Activity - waits for a specific file to appear or disappear and launches the processing.
    • EndLoop Activity
    • Exception Handler
    • Execute Command
    • Nested Condition
    • Routine Activity
    • StartLoop Activity
    • UserVariables Activity

    Datastage -Functions in Transformer


    1.  AlNum(%string%)
    Allnum(“12345”) --- output-1
    Allnum(“abcd”) --- output-1
    Allnum(“ab125”) --- output-1
    Allnum(“@a12345”) --- output-0
    Allnum(“@a1 2345”) --- output-0
    Except Special Symbols (Letters & Numbers) it will give output as “1”
    2. Alpha(%string%)
    Alpha (“abcd”) --- output-1
    Alpha (“12345”) --- output-0
    Alpha (“ab125”) --- output-0
    Alpha (“@a12345”) --- output-0
    Alpha (“@a1 2345”) --- output-0
    3. Compactwhitespace(%string%)
         It reduces or replaces multiple spaces into single space.
    Ex: CompactWhiteSpace("sud hee                  r") 
                Out Put: sud hee r
    4. Space(length):

         Inserts no.of white spaces given in lengthy parameters.
            Ex:-Space(6):’Brahma’
           Output: Brahma

    5 . Len(string):
    Returns length of string in characters
         Ex:- Len(space(6):’Brahma’)
         Output---12
         Ex:- Len(“Brahma”)
          Output—6
    6. Compare(String1,String2,[Justification]):       //case sensitive
          Compares String1 & String2,if String1 is greater than String2 it will result 1
                If String1 is less than String2,then it will result 1.
                If both the strings are equal then it will result 0.
                To compare String1 with String2, we give Justification:L
               To compare String2 with String1,we give Justification:R
         Ex:       Compare(“abcd”,”abc”,”L”)---output-1
                      Compare(“abcd”,”abc”,”R”)---output-0
                      Compare(“abc”,”Abc”)---output-1     Default Justification-L
                      Compare(“abc”,”abcd”)---output  -1
    7. CompareNoCase(String1,String2):
                 Same as Compare,but diff is this function is NOT case sensitive.
              In this function no argument called Justification.
    8. CompareNum(String1,String2,length):
               Compares two strings up to the given length.
               Ex:-     CompareNum(“a”,”abc”,1)     output---0
                           CompareNum(“a”,”abcde”,5) output--    -1
               CompareNum(“abcde”,”a”,5) output---1
               CompareNum(“abcde”,”Abcde”,1)    output---1
               CompareNum(“Abcde”,”abcde”,1)    output--   -1
    9. CompareNumNoCase(String1,String2,length):
              Same as CompareNum, diff is this function is NOT case sensitive.
              Ex:-     CompareNumNoCase(“Abcde”,”abcde”,5)   output---0
             CompareNumNoCase(“abxye”,”abcde”,5)    output---1
             CompareNumNoCase(“abcde”,”abxde”,5)    output--  -1
    10. Upcase(String):
               Change all lowercase letters in a string to uppercase.
               Ex:-     Upcase(“brahma”)       output—BRAHMA
               Upcase(“braHma”)      output----BRAHMA
    11. DQuote(String):
              Enclose a string in double quotation marks.
             Ex:-     DQuote(Upcase(“brahma”))   output---“BRAHMA”
            DQuote(‘brahma’)                  output---“brahma”
    12. Field(String,delimiter,occurrence,number):
              Returns the substring before delimiter based on occurrence we are given.
                Ex:-     Field(“br_ah_ma_na”,”_”,2)   output---ah
             Here ah is substring before the second occurrence of delimiter ‘_’.
    Field(“br_ah_ma_na”,”_”,3)   output---ma
             Here after ma _ is third occurrence.
    Field(“br_ah_ma_na”,”_”,1,4)            output---br_ah_ma_na
    Field(“br_ah_ma_na”,”_”,1,2)            output---br_ah
    Field(“br_ah_ma_na”,”_”,1,3)            output---br_ah_ma
    Field(“br_ah_ma_na_a”,”_”,1,5)        output---br_ah_ma_na_a
     Note:-
              Field(“brahmananda”,”a”,2)   output—hm
       Here ‘a’ is delimiter
             Field(“brbhmananda”,”a”,2)   output—n
             Field(“brhhmananda”,”a”,1)   output—brhhm
              Field(“brahmananda”,”a”,1,3)  output—brahman

    13. Index(String,substring,occurrence):
            Returns starting character position of substring.
            Ex:-     Index(“brahma”,”a”,1)            output---3
            Index(“brahma”,”a”,2)            output---6
            Index(“brahmananda”,”a”,3)  output---8
            Index(“brahmananda”,”na”,1)  output---7
    14. Convert(fromlist,tolist,Expression)
          Converts specified character in a string(given in expression arg) to designated replacement character
          Ex:-  convert(“a”,”y”,”brahma”)
       o/p:byhmy
                 convert(“ah”,”y”,”brahma”)
       o/p:brymy
          This function performs character replacement, but not word.
          Ex:  convert(“brahma”,”msrmad”,”brahma”)
    15. Count(String,Substring):
          Count number of times a Substring occurs in a String.
          Ex:Count(“brahma”,”a”)
                o/p:2
               Count(“brahmabaabaaba”,”ab”)
                o/p:3
              Count(“brahmabaabaaba”,”ba”)
                o/p:3
    16. DCount(String,Delimiter):
          Count number of delimited fields in a string.
         Ex:  DCount(“br-ah-ma-na”,”_”)-------o/p:4
                 DCount(“br,ah,ma,na”,”,”)-------o/p:5
                 DCount(“br,ah-ma,na”,”,”)-------o/p:4
        ah-ma, treats as one field
    17. Downcase(String):
           Change all uppercase letters in a String to lowercase
         Ex:  Downcase(“BRAHMA”)------o/p:brahma
                 Downcase(“brHMA”)------o/p:brahma
                 Downcase(“BRAhma”)------o/p:brahma
    18. Left(String,length)
            Returns leftmost ‘n’ characters of the string, where n is length.
          
           Ex:  Left(“brahmananda”,4) o/p:brah
                   Left(“bra__hmananda”,6) o/p:bra_hm
    19. Right(String,Length)
          Returns rightmost ‘n’ characters of the string
        
          Ex:   Right(“brahmananda”,4) o/p:anda
    20. Num(String)
         Returns 1 if string can be converted to a number.
         
         Ex:    Num(“brah”)    o/p=0
                  Num(“369”)     o/p=1
    21. PadString(String,PadString,Padlength)
           Returns the string padded with the optional pad character and padlength is the number of times it add to the original string
        
          Ex:    PadString(“brahma”,”+”,5)    o/p:brahma+++++
                    PadString(“brahma”,”reddy”,5)   o/p:brahmarrrrr
    22. Str(String,repeats)
           Repeats the input string no.of times given in repeats
        
          Ex:  Str(“mbnr”,2)    o/pmbnrmbnr
    23. StripWhiteSpace(String)
           Returns the string after stripping (removing) all white space from it.
         
         Ex:  StripWhiteSpace(“br a h ma”)   o/p:brahma

    Wednesday, October 14, 2015

    Top 20 Datastage Interview Questions

    1. What is the flow of load loading data into fact & dimensional tables?
    A) Fact table - Table with Collection of Foreign Keys corresponding to the Primary
    Keys in Dimensional table. Consists of fields with numeric values.
    Dimension table - Table with Unique Primary Key.
    Load - Data should be first loaded into dimensional table. Based on the primary key
    values in dimensional table, the data should be loaded into Fact table.
    2. What is the default cache size? How do you change the cache size if needed?
    A. Default cache size is 256 MB. We can increase it by going into Datastage
    Administrator and selecting the Tunable Tab and specify the cache size over there.
    3. What are types of Hashed File?
    A) Hashed File is classified broadly into 2 types.
    a) Static - Sub divided into 17 types based on Primary Key Pattern.
    b) Dynamic - sub divided into 2 types
    i) Generic ii) Specific.
    Dynamic files do not perform as well as a well, designed static file, but do perform better
    than a badly designed one. When creating a dynamic file you can specify the following
    Although all of these have default values)
    By Default Hashed file is "Dynamic - Type Random 30 D"
    4. What does a Config File in parallel extender consist of?
    A) Config file consists of the following.
    a) Number of Processes or Nodes.
    b) Actual Disk Storage Location.
    5. What is Modulus and Splitting in Dynamic Hashed File?
    A. In a Hashed File, the size of the file keeps changing randomly.
    If the size of the file increases it is called as "Modulus".
    If the size of the file decreases it is called as "Splitting".
    6. What are Stage Variables, Derivations and Constants?
    A. Stage Variable - An intermediate processing variable that retains value during read
    and doesn’t pass the value into target column.
    Derivation - Expression that specifies value to be passed on to the target column.
    Constant - Conditions that are either true or false that specifies flow of data with a link.
    7. Types of views in Datastage Director?
    There are 3 types of views in Datastage Director
    a) Job View - Dates of Jobs Compiled.
    b) Log View - Status of Job last run
    c) Status View - Warning Messages, Event Messages, Program Generated Messages.
    8. Types of Parallel Processing?
    A) Parallel Processing is broadly classified into 2 types.
    a) SMP - Symmetrical Multi Processing.
    b) MPP - Massive Parallel Processing.
    9. Orchestrate Vs Datastage Parallel Extender?
    A) Orchestrate itself is an ETL tool with extensive parallel processing capabilities and
    running on UNIX platform. Datastage used Orchestrate with Datastage XE (Beta version
    of 6.0) to incorporate the parallel processing capabilities. Now Datastage has purchased
    Orchestrate and integrated it with Datastage XE and released a new version Datastage 6.0
    i.e Parallel Extender.
    10. Importance of Surrogate Key in Data warehousing?
    A) Surrogate Key is a Primary Key for a Dimension table. Most importance of using it is
    it is independent of underlying database. i.e. Surrogate Key is not affected by the changes
    going on with a database.
    11. How to run a Shell Script within the scope of a Data stage job?
    A) By using "ExcecSH" command at Before/After job properties.
    12. How to handle Date conversions in Datastage? Convert a mm/dd/yyyy format to
    yyyy-dd-mm?
    A) We use a) "Iconv" function - Internal Conversion.
    b) "Oconv" function - External Conversion.
    Function to convert mm/dd/yyyy format to yyyy-dd-mm is
    Oconv(Iconv(Filedname,"D/MDY[2,2,4]"),"D-MDY[2,2,4]")
    13 How do you execute datastage job from command line prompt?
    A) Using "dsjob" command as follows.
    dsjob -run -jobstatus projectname jobname
    14. Functionality of Link Partitioner and Link Collector?
    A)Link Partitioner: It actually splits data into various partitions or data flows using
    various partition methods.
    Link Collector: It collects the data coming from partitions, merges it into a single data
    flow and loads to target.
    15. Types of Dimensional Modeling?
    A) Dimensional modeling is again sub divided into 2 types.
    a) Star Schema - Simple & Much Faster. Denormalized form.
    b) Snowflake Schema - Complex with more Granularity. More normalized form.
    16. Differentiate Primary Key and Partition Key?
    A)Primary Key is a combination of unique and not null. It can be a collection of key values
    called as composite primary key. Partition Key is a just a part of Primary Key. There are
    several methods of partition like Hash, DB2, and Random etc. While using Hash partition
    we specify the Partition Key.
    17. Differentiate Database data and Data warehouse data?
    A) Data in a Database is
    a) Detailed or Transactional
    b) Both Readable and Writable.
    c) Current.
    18. Containers Usage and Types?
    A)Container is a collection of stages used for the purpose of Reusability.
    There are 2 types of Containers.
    a) Local Container: Job Specific
    b) Shared Container: Used in any job within a project.
    19. Compare and Contrast ODBC and Plug-In stages?
    A)ODBC: a) Poor Performance.
    b) Can be used for Variety of Databases.
    c) Can handle Stored Procedures.
    Plug-In: a) Good Performance.
    b) Database specific. (Only one database)
    c) Cannot handle Stored Procedures.
    20. Dimension Modelling types along with their significance
    A)Data Modelling is Broadly classified into 2 types.
    a) E-R Diagrams (Entity - Relatioships).
    b) Dimensional Modelling.

    Tuesday, October 13, 2015

    How to Start and Stop the Datastage Engine

    Here are the instructions to Stop Information Server 8.x, verify all the processes are stopped and then restart Information Server (see instructions below for each step).

    We will stop in this order:

    •       Stop DataStage
    •       Stop Node Agents
    •       Stop WebSphere

    1.     Log into your Unix/Linux machine as root (you can run first part as dsadm). 
    2.     Make sure that no one is using DataStage. 
    3.     Check if there are any processes left by executing the following commands:

    • ps -ef | grep phantom - This would show processes from jobs
    • ps -ef | grep osh - This would show processes for parallel jobs
    • ps -ef | grep dsapi - This would show processes for client connection
    • ps -ef | grep dscs - This would show processes for client connection

    These commands shouldn't return any results. If they return results, it means that there are still some processes. You need to wait for the users to stop their processes or kill the processes (using kill -9 PID) if you confirm that these are not longer valid connections .

    4.     Check if DataStage RPC Daemon (dsrpc) is running by executing:

    • netstat -a | grep dsrpc

    If dsrpc is running then you should see one line with the status LISTEN. If you get more lines it means that there are still some connections and you need to wait. If you see that dsrpc or other connections are in status CLOSE_WAIT then you also need to wait until these disappear (you can also reboot the machine)

    5.     Stop DataStage (you have to run this from $DSHOME)

    • . ./dsenv
    • bin/uv -admin -stop

    6.     Check to see if there are any shared memory segments left:

    • ipcs -m | grep ade

    Note: If you are not running with the default itag of "ade" use that instead of "ade". For example if you have two DataStage instances one would be "ade" and the second would have another tag, say "801" or "efg".
    If you get a result, take note of the ID and remove it (you will need root access for this part)
    ipcrm -m ID

    7.     Then check dsrpc again using


    • netstat -a | grep dsrpc

    You shouldn't get any results. If you get any and/or the dsrpc is in CLOSE_WAIT then you need to wait until this it disappears (you can also reboot the machine)

    8.     Stop the ASB Agent (you need to be root here)


    • Go to <InstallPath>/IBM/InformationServer/ASBNode/bin
    • Run ./NodeAgents.sh stop

    Type 'yes' if you receive the following message: rm: remove write-protected regular file Agent.pid'?

    9.     Check the agents have stopped:

    • ps -ef | grep java

    To see full command in Solaris:

    • /usr/ucb/ps -auxww | grep java


    If still see the Logging Agent or ASB Agent then you need to kill these. e.g. kill -9 PID

    10.  Stop the WebSphere Server (you need to be root here)

    • Go to <InstallPath>/IBM/WebSphere/AppServer/bin
    • Run ./stopServer.sh server1

    * if that does not work, try ./bin/stopServer.sh server1 -username wasadmin -password wasadmin
    Now check that the WebSphere java processes is gone:

    • ps -ef | grep java

    To see full command in Solaris:

    • /usr/ucb/ps -auxww | grep java

    remove it if it exists with: kill PID (no -9 in the kill command)
    Now, you can restart, in this order:


    •  Start WebSphere Server
    •   Start ASB Agent
    •  Start DataStage

    1.     Start WebSphere Server as root

    • Go to <InstallPath>/IBM/WebSphere/AppServer/bin
    • Run ./startServer.sh server1

    Wait until you get back a line saying: Server server1 open for e-business;

    2.     Start ASB Agent as root

    • Go to <InstallPath>/IBM/InformationServer/ASBNode/bin
    • Run ./NodeAgents.sh start

    3.     Start DataStage (as dsadm)


    • Go to $DSHOME
    • Run bin/uv -admin -start

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