Difference between Informatica and Datastage
Both Datastage and Informatica are powerful ETL
tools . Both tools
do almost exactly the same thing in almost exactly the same way. Performance,
maintainability, learning curve are all similar and comparable. Below are the few things which I would like highlight regarding both these
tools.
Multiple
Partitions
Informatica offers partitioning as dynamic partitioning which defaults a
workflow not at every Stage/Object level in a mapping/job. Informatica offers
other partitioning choices as well at the workflow level.
DataStage's pipeline partitioning uses multiple partitions, processed and then
re-collected with DataStage. DataStage lets control a job design based on
the logic of the processing instead of defaulting the whole pipeline flow to
one partition type. DataStage offers 7
different types of multi-processing partitions.
User Interface
Informatica offers access to the development and monitoring effort through its 4 GUIs - offered as Informatica PowerDesigner, Repository Manager, Worflow Designer, Workflow Manager.
DataStage caters to development and monitoring its jobs through 3 GUIs - IBM DataStage Designer(for development), Job Sequence Designer(workflow design) and Director(for monitoring).
Informatica offers access to the development and monitoring effort through its 4 GUIs - offered as Informatica PowerDesigner, Repository Manager, Worflow Designer, Workflow Manager.
DataStage caters to development and monitoring its jobs through 3 GUIs - IBM DataStage Designer(for development), Job Sequence Designer(workflow design) and Director(for monitoring).
Version Control
Informatica offers instant version control through its repository server managed with “Repository Manager” GUI console. A mapping with work-in-progress cannot be opened until saved and checked back into the repository. Version control is done by using checkin and check out.
Informatica offers instant version control through its repository server managed with “Repository Manager” GUI console. A mapping with work-in-progress cannot be opened until saved and checked back into the repository. Version control is done by using checkin and check out.
Version Control was offered as a component until version Ascential
DataStage7.5.x. Ascential was acquired by IBM and when DataStage was integrated
into IBM Information Server with DataStage at version 8.0.1, the support of
version control as a component was discontinued.
Repository based
flow
Informatica, offers a step-by-step effort of creating a data integration solution. Each object created while mapping a source with a target gets saved into the repository project folder categorized by - Sources, Targets, Transformations, Mappings, Mapplets, User-defined functions, Business Components, Cubes and Dimensions. Each object created can be shared, dropped into a mapping across cross-functional development teams. Thus increasing re-usability. Projects are folder based and inter-viewable.
DataStage offers a project based integration solution, projects are not interviewable. Every project needs a role based access. The step-by-step effort in mapping a source to a target lineages into a job. For sharing objects within a job, separate objects need to be created called containers that are local/shared.
Informatica, offers a step-by-step effort of creating a data integration solution. Each object created while mapping a source with a target gets saved into the repository project folder categorized by - Sources, Targets, Transformations, Mappings, Mapplets, User-defined functions, Business Components, Cubes and Dimensions. Each object created can be shared, dropped into a mapping across cross-functional development teams. Thus increasing re-usability. Projects are folder based and inter-viewable.
DataStage offers a project based integration solution, projects are not interviewable. Every project needs a role based access. The step-by-step effort in mapping a source to a target lineages into a job. For sharing objects within a job, separate objects need to be created called containers that are local/shared.
Data Encryption
Informatica has an offering within PowerCenter Designer as a separate transformation called “Data Masking Transformation”.
Informatica has an offering within PowerCenter Designer as a separate transformation called “Data Masking Transformation”.
Data Masking or encryption
needs to be done before reaching DataStage Server.
Variety of
Transformations
Informatica offers about 30 general transformations for processing incoming data.
Informatica offers about 30 general transformations for processing incoming data.
Datastage offers about 40 data transforming stages/objects. Datastage is more powerful transformation engine
by using functions (Oconv and IConv) and routines. We can do almost any
transformation.
Source_- Target
flow
Within Informatica’s PowerCenter Designer, first a source definition needs to be created using “Source Analyzer” that imports the metadata, then a target definition is created using “Target Designer”, then a transformation using “Transformation Developer” is created, and finally maps a source-transformation-target using “Mapping Designer”.
Within Informatica’s PowerCenter Designer, first a source definition needs to be created using “Source Analyzer” that imports the metadata, then a target definition is created using “Target Designer”, then a transformation using “Transformation Developer” is created, and finally maps a source-transformation-target using “Mapping Designer”.
Datastage lets drag and
drop a functionality i.e a stage within in one canvas area for a pipeline
source-target job. With DataStage within
the “DataStage Designer” import of both source and target metadata is needed,
proceeding with variety of stages offered as database stages, transformation
stages, etc.
The biggest difference between both the vendor offerings in this area is Informatica forces you to be organized through a step-by-step design process, while DataStage leaves the organization as a choice and gives you flexibility in dragging and dropping objects based on the logic flow.
The biggest difference between both the vendor offerings in this area is Informatica forces you to be organized through a step-by-step design process, while DataStage leaves the organization as a choice and gives you flexibility in dragging and dropping objects based on the logic flow.
Checking
Dependencies
Informatica offers a separate edition – Advanced edition that helps with data lineage and impact analysis. We can go to separate targets and source and check all the dependencies on that.
DataStage offers through Designer by right clicking on a job to perform dependencies or impact analysis.
Informatica offers a separate edition – Advanced edition that helps with data lineage and impact analysis. We can go to separate targets and source and check all the dependencies on that.
DataStage offers through Designer by right clicking on a job to perform dependencies or impact analysis.
Components Used
The Informatica ETL
transformations are very specific purpose, so you tend to need more boxes on
the page to do the same thing. eg. A simple transform in Informatica would have
a Source Table, Source Qualifier, Lookup, Router, 2 Update Strategies, and 2
Target Tables (9 boxes).
In DataStage,
you would have a
Table and Hashed File for the lookup, plus a Source Relational Stage,
Transformation Stage, and 2 links to a target Relational Stage (5
boxes). This visual clutter in Informatica is a bit annoying.
Type of link
To link two components in
Informatica, you have to link at the column level.We have to connect each and
every column bw the two componenents
In DataStage, you link at the component level, and
then map individual columns. This allows you to have coding templates that are
all linked up - just add columns. I find this a big advantage in DS.
Reusability
Informatica offers ease of re-usability through Mapplets and Worklets for re-using mappings and workflows.This really improves the performance
DataStage offers re-usability of a job through containers(local&shared). To re-use a Job Sequence(workflow), you will need to make a copy, compile and run.
Informatica offers ease of re-usability through Mapplets and Worklets for re-using mappings and workflows.This really improves the performance
DataStage offers re-usability of a job through containers(local&shared). To re-use a Job Sequence(workflow), you will need to make a copy, compile and run.
Code Generation
and Compilation
Informatica’s thrust is the auto-generated code. A mapping gets created by dropping a source-transformation-target that doesn’t need to be compiled.
DataStage requires to compile a job in order to run it successfully.
Informatica’s thrust is the auto-generated code. A mapping gets created by dropping a source-transformation-target that doesn’t need to be compiled.
DataStage requires to compile a job in order to run it successfully.
Heterogeneous Sources
In Informatica we can use both heterogenous source and homogenous source.
Datastage does not perform very well with heterogeneous sources. You might end up extracting data from all the sources and putting them into a hash and start your transformation
Slowly Changing Dimension
Informatica supports Full
History, Recent Values, Current & Previous Values using SCD wizards.
DataStage supports only through
Custom scripts and does not have a wizard to do this
Dynamic Lookup Cache
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