Example Of Surrogate Key Generator Stage In Datastage

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by consneogercesili 2020. 9. 2. 17:37

Example Of Surrogate Key Generator Stage In Datastage 4
Surrogate Key Generator Stage In Datastage
Example Of Surrogate Key Generator Stage In Datastage Training

DataStage and QualityStage stages are grouped into the following logical sections:

Aug 19, 2018 Surrogate Key Generator Stage in DataStage The Surrogate Key Generator stage is a processing stage that generates surrogate key columns and maintains the key source. A surrogate key is a unique primary key that is not derived from the data that it represents, therefore changes to the data will not change the primary key. Apr 26, 2011 Generate Surrogate Key without Surrogate Key Stage April 26, 2011 ukatru Leave a comment Go to comments Use the following formula in Transformer stage to generate a surrogate key.

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 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

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.
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.
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

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.
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.
Funnel combines mulitple streams into one.
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).
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.
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.
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.
Sort sorts input columns
Transformer stage handles extracted data, performs data validation, conversions and lookups.
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.

File stage types

Example Of Surrogate Key Generator Stage In Datastage 4

Sequential file is used to read data from or write data to one or more flat (sequential) files.
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)
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.
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

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

Key generator download Datastage

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

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.
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 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.