1. List the types of statistics collected for monitoring the functioning of the data
warehouse.
2. Describe any six different types of action for growth planning prompted by the
collected statistics.
3. How do the statistics help in fine-tuning the data warehouse?
4. Do you think publishing the statistics and similar data for the users is helpful? If
so, why?
5. What are the three main subjects in user training content? Why are these essential?
6. Describe any four major tasks needed to prepare a training program.
7. What are the responsibilities of the training administrator?
8. Do you think a multitier user support structure is suitable for the data warehouse
environment? What are the alternatives?
9. What role can the company’s intranet play in training and user support?
10. List any five factors to be part of ETL management.
Wednesday, November 18, 2009
Evluating Vendor Solutions
First and foremost, determine the functions in your data warehouse that absolutely
need vendor tools and solutions.
For each type of product you need, carefully list the features that are expected. Divide
the features into groups by importance—high, medium, and low. Use these
groups of features to grade the products you are considering.
Allocate enough time to research available solutions and vendors thoroughly.
If you try to incorporate solutions from too many different vendors, you must be
prepared to face serious challenges of incompatibilities and restrictions for integration.
Stay with two or three vendors whose products are most appropriate for your
needs.
Metadata is a key component of a data warehouse. Ensure that the vendor products
you choose can handle metadata satisfactorily.
The standing and stability of the vendor is equally important as the effectiveness of
the products themselves. Even when the products are suitable for your environment,
if you are concerned about the staying power of the vendor, have second thoughts on
selecting these products.
Never rely solely on vendor demonstrations as the basis for your selection, nor
should you check only the references furnished by the vendors themselves.
Test the tools and products in your environment, with subsets of your own data.
Arrange for both user representatives and IT members of the project team to test the
products, jointly and independently.
Establish a definitive method for comparing and scoring competing products. You
may devise a point system to score the various features you are looking for in a
product type.
The success of your data warehouse rides on the end-user tools. Pay special attention
to the choice of the end-user tools. You may compromise a bit on the other
types, but not on the end-user tools.
Most of your end-users will be new to data warehousing. Good, intuitive and easyto-
use tools go a long way in winning them over.
Users like tools that seamlessly incorporate online queries, batch reporting, and data
extraction for analysis.
need vendor tools and solutions.
For each type of product you need, carefully list the features that are expected. Divide
the features into groups by importance—high, medium, and low. Use these
groups of features to grade the products you are considering.
Allocate enough time to research available solutions and vendors thoroughly.
If you try to incorporate solutions from too many different vendors, you must be
prepared to face serious challenges of incompatibilities and restrictions for integration.
Stay with two or three vendors whose products are most appropriate for your
needs.
Metadata is a key component of a data warehouse. Ensure that the vendor products
you choose can handle metadata satisfactorily.
The standing and stability of the vendor is equally important as the effectiveness of
the products themselves. Even when the products are suitable for your environment,
if you are concerned about the staying power of the vendor, have second thoughts on
selecting these products.
Never rely solely on vendor demonstrations as the basis for your selection, nor
should you check only the references furnished by the vendors themselves.
Test the tools and products in your environment, with subsets of your own data.
Arrange for both user representatives and IT members of the project team to test the
products, jointly and independently.
Establish a definitive method for comparing and scoring competing products. You
may devise a point system to score the various features you are looking for in a
product type.
The success of your data warehouse rides on the end-user tools. Pay special attention
to the choice of the end-user tools. You may compromise a bit on the other
types, but not on the end-user tools.
Most of your end-users will be new to data warehousing. Good, intuitive and easyto-
use tools go a long way in winning them over.
Users like tools that seamlessly incorporate online queries, batch reporting, and data
extraction for analysis.
Friday, September 18, 2009
Which transformation should we use to normalize the COBOL and relational sources?
Normalizer Transformation
When U drag the COBOL source in to the mapping Designer workspace,the normalizer transformation automatically appears,creating input and output ports for every column in the source
When U drag the COBOL source in to the mapping Designer workspace,the normalizer transformation automatically appears,creating input and output ports for every column in the source
Thursday, September 17, 2009
Difference between static cache and dynamic cache
Static cache Dynamic cache
U can not inert or update the cache. U can insert rows into the cache as u pass
to the target
The informatic server returns a value from The informatic server inserts rows into cache
the lookup table opr cache when the condition when the condition is false.This indicates that
is true.When the condition is not true,the the row is not in the cache or target table
informatica server returns the default value U can pass these rows to the target table
for connected transformations and null for
unconnected transformations
i
U can not inert or update the cache. U can insert rows into the cache as u pass
to the target
The informatic server returns a value from The informatic server inserts rows into cache
the lookup table opr cache when the condition when the condition is false.This indicates that
is true.When the condition is not true,the the row is not in the cache or target table
informatica server returns the default value U can pass these rows to the target table
for connected transformations and null for
unconnected transformations
i
Wednesday, September 16, 2009
What r the types of lookup caches?
Persistent cache: U can save the lookup cache files and reuse them the next time the informatica server processes a lookup transformation configured to use the cache
Recache from database: If the persistent cache is not synchronized with he lookup table,U can configure the lookup transformation to rebuild the lookup cache
Static cache: U can configure a static or read-only cache for only lookup table. By default informatica server creates a static cache.It caches the lookup table and lookup values in the cache for each row that comes into the transformation.when the lookup condition is true,the informatica server does not update the cache while it prosesses the lookup transformation
Dynamic cache: If u want to cache the target table and insert new rows into cache and the target,u can create a look up transformation to use dynamic cache.The informatica server dynamically inerts data to the target table
shared cache: U can share the lookup cache between multiple transactions.U can share unnamed cache between transformations inthe same maping
Recache from database: If the persistent cache is not synchronized with he lookup table,U can configure the lookup transformation to rebuild the lookup cache
Static cache: U can configure a static or read-only cache for only lookup table. By default informatica server creates a static cache.It caches the lookup table and lookup values in the cache for each row that comes into the transformation.when the lookup condition is true,the informatica server does not update the cache while it prosesses the lookup transformation
Dynamic cache: If u want to cache the target table and insert new rows into cache and the target,u can create a look up transformation to use dynamic cache.The informatica server dynamically inerts data to the target table
shared cache: U can share the lookup cache between multiple transactions.U can share unnamed cache between transformations inthe same maping
Tuesday, September 15, 2009
what is meant by lookup caches?
The informatica server builds a cache in memory when it processes the first row af a data in a cached look up transformation.It allocates memory for the cache based on the amount u configure in the transformation or session properties.The informatica server stores condition values in the index cache and output values in the data cache
Monday, September 14, 2009
Differences between connected and unconnected lookup?
Connected lookup Unconnected lookup
Receives input values diectly from Receives input values from the result of a
the pipe line. lkp expression in a another transformation
U can use a dynamic or static cache U can use a static cache
Cache includes all lookup columns Cache includes all lookup out put ports in the
used in the mapping lookup condition and the lookup/return port
Support user defined default values Does not support user defiend default values
Receives input values diectly from Receives input values from the result of a
the pipe line. lkp expression in a another transformation
U can use a dynamic or static cache U can use a static cache
Cache includes all lookup columns Cache includes all lookup out put ports in the
used in the mapping lookup condition and the lookup/return port
Support user defined default values Does not support user defiend default values
Sunday, September 13, 2009
Why use the lookup transformation ?
To perform the following tasks
Get a related value. For example, if your source table includes employee ID, but you want to include the employee name in your target table to make your summary data easier to read.
Perform a calculation. Many normalized tables include values used in a calculation, such as gross sales per invoice or sales tax, but not the calculated value (such as net sales).
Update slowly changing dimension tables. You can use a Lookup transformation to determine whether records already exist in the target.
Get a related value. For example, if your source table includes employee ID, but you want to include the employee name in your target table to make your summary data easier to read.
Perform a calculation. Many normalized tables include values used in a calculation, such as gross sales per invoice or sales tax, but not the calculated value (such as net sales).
Update slowly changing dimension tables. You can use a Lookup transformation to determine whether records already exist in the target.
Saturday, September 12, 2009
What is the look up transformation?
Use lookup transformation in u'r mapping to lookup data in a relational table, view, synonym
Informatica server queries the look up table based on the lookup ports in the transformation. It compares the lookup transformation port values to lookup table column values based on the look up condition
Informatica server queries the look up table based on the lookup ports in the transformation. It compares the lookup transformation port values to lookup table column values based on the look up condition
Thursday, September 10, 2009
Wednesday, September 9, 2009
What r the settings that u use to configure the joiner transformation?
Master and detail source
Type of join
Condition of the join
Type of join
Condition of the join
Tuesday, September 8, 2009
which condtions we can not use joiner transformation(Limitaions of joiner transformation)?
Both pipelines begin with the same original data source.
Both input pipelines originate from the same Source Qualifier transformation.
Both input pipelines originate from the same Normalizer transformation.
Both input pipelines originate from the same Joiner transformation.
Either input pipelines contains an Update Strategy transformation.
Either input pipelines contains a connected or unconnected Sequence Generator transformation.
Both input pipelines originate from the same Source Qualifier transformation.
Both input pipelines originate from the same Normalizer transformation.
Both input pipelines originate from the same Joiner transformation.
Either input pipelines contains an Update Strategy transformation.
Either input pipelines contains a connected or unconnected Sequence Generator transformation.
Monday, September 7, 2009
What r the diffrence between joiner transformation and source qualifier transformation?
U can join hetrogenious data sources in joiner transformation which we can not achieve in source qualifier transformation
U need matching keys to join two relational sources in source qualifier transformation.Where as u doesn't need matching keys to join two sources
Two relational sources should come from same datasource in sourcequalifier.U can join relatinal sources which r coming from diffrent sources also
U need matching keys to join two relational sources in source qualifier transformation.Where as u doesn't need matching keys to join two sources
Two relational sources should come from same datasource in sourcequalifier.U can join relatinal sources which r coming from diffrent sources also
Sunday, September 6, 2009
What is aggregate cache in aggregator transforamtion?
The aggregator stores data in the aggregate cache until it completes aggregate calculations.When u run a session that uses an aggregator transformation,the informatica server creates index and data caches in memory to process the transformation.If the informatica server requires more space,it stores overflow values in cache files
Saturday, September 5, 2009
Can u use the maping parameters or variables created in one maping into any other reusable transformation?
Yes.Because reusable tranformation is not contained with any maplet or maping
Can U use the maping parameters or variables created in one maping into another maping?
NO
We can use mapping parameters or variables in any transformation of the same maping or mapplet in which U have created maping parameters or variables
We can use mapping parameters or variables in any transformation of the same maping or mapplet in which U have created maping parameters or variables
Friday, September 4, 2009
What r the mapping paramaters and maping variables?
Maping parameter represents a constant value that U can define before running a session.A mapping parameter retains the same value throughout the entire session
When u use the maping parameter ,U declare and use the parameter in a maping or maplet.Then define the value of parameter in a parameter file for the session
Unlike a mapping parameter,a maping variable represents a value that can change throughout the session.The informatica server saves the value of maping variable to the repository at the end of session run and uses that value next time U run the session
When u use the maping parameter ,U declare and use the parameter in a maping or maplet.Then define the value of parameter in a parameter file for the session
Unlike a mapping parameter,a maping variable represents a value that can change throughout the session.The informatica server saves the value of maping variable to the repository at the end of session run and uses that value next time U run the session
Thursday, September 3, 2009
What r the unsupported repository objects for a mapplet?
COBOL source definition
Joiner transformations
Normalizer transformations
Non reusable sequence generator transformations
Pre or post session stored procedures
Target definitions
Power mart 3.5 style Look Up functions
XML source definitions
IBM MQ source definitions
Joiner transformations
Normalizer transformations
Non reusable sequence generator transformations
Pre or post session stored procedures
Target definitions
Power mart 3.5 style Look Up functions
XML source definitions
IBM MQ source definitions
Wednesday, September 2, 2009
What r the methods for creating reusable transforamtions?
Two methods
1.Design it in the transformation developer
2.Promote a standard transformation from the mapping designer.After U add a transformation to the mapping , U can promote it to the status of reusable transformation
Once U promote a standard transformation to reusable status,U can demote it to a standard transformation at any time
If u change the properties of a reusable transformation in mapping,U can revert it to the original reusable transformation properties by clicking the revert button
1.Design it in the transformation developer
2.Promote a standard transformation from the mapping designer.After U add a transformation to the mapping , U can promote it to the status of reusable transformation
Once U promote a standard transformation to reusable status,U can demote it to a standard transformation at any time
If u change the properties of a reusable transformation in mapping,U can revert it to the original reusable transformation properties by clicking the revert button
Tuesday, September 1, 2009
What r the reusable transformations?
Reusable transformations can be used in multiple mappings.When u need to incorporate this transformation into maping,U add an instance of it to maping.Later if U change the definition of the transformation ,all instances of it inherit the changes.Since the instance of reusable transforamation is a pointer to that transforamtion,U can change the transforamation in the transformation developer,its instances automatically reflect these changes.This feature can save U great deal of work
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