✍ Kashif M

Saturday, 24 October 2015

OBIEE 12c realised!!!!

Yuppppy,  long awaited OBIEE 12c has been released.  I can't wait to download and check it out.

Here is the download link of OBIEE 12c



Lets download OBIEE 12c and have fun.

Warm Regards
Kashif M

Wednesday, 7 October 2015

Joiner Transformation Interview Question ( Part - 1)

What is a Joiner Transformation and why it is an Active one?

A Joiner is an Active and Connected transformation used to join two source data streams coming from same or heterogeneous databases or files.

The Joiner transformation joins sources with at least one matching column. The Joiner transformation uses a condition that matches one or more pairs of columns between the two sources.

In the Joiner transformation, we must configure the transformation properties namely Join Condition, Join Type and optionally Sorted Input option to improve Integration Service performance.

The join condition contains ports from both input sources that must match for the Integration Service to join two rows. Depending on the join condition and the type of join selected, the Integration Service either adds the row to the result set or discards the row. Because of this reason, the number of rows in Joiner output may not be equal to the number of rows in Joiner Input. This is why Joiner is considered an Active transformation.

State the limitations where we cannot use Joiner in the mapping pipeline.


The Joiner transformation accepts input from most transformations. However, following are the limitations:

Joiner transformation cannot be used when either of the input pipelines contains an Update Strategy transformation.

Joiner transformation cannot be used if we connect a Sequence Generator transformation directly before the Joiner transformation.

Out of the two input pipelines of a joiner, which one will we set as the master pipeline?

During a session run, the Integration Service compares each row of the master source against the detail source. The master and detail sources need to be configured for optimal performance.

When the Integration Service processes an unsorted Joiner transformation, it blocks the detail source while it caches rows from the master source. Once the Integration Service finishes reading and caching all master rows, it unblocks the detail source and reads the detail rows. This is why if we have the source containing fewer input rows in master, the cache size will be smaller, thereby improving the performance.
For a Sorted Joiner transformation, use the source with fewer duplicate key values as the master source for optimal performance and disk storage. When the Integration Service processes a sorted Joiner transformation, it caches rows for one hundred keys at a time. If the master source contains many rows with the same key value, the Integration Service must cache more rows, and performance can be slowed.

What are the different types of Joins available in Joiner Transformation?


In SQL, a join is a relational operator that combines data from multiple tables into a single result set. The Joiner transformation is similar to an SQL join except that data can originate from different types of sources.

The Joiner transformation supports the following types of joins:


Master Outer

Detail Outer

Full Outer

Joiner Transformation Interview Question ( Part - 1)
Joiner Transformation Interview Question ( Part - 1)

A normal or master outer join performs faster than a full outer or detail outer join.

Tuesday, 6 October 2015

Expression Transformation Interview Question ( Part - 2)

Describe the approach for the requirement. Suppose the input is:


The desired output is:


Answer: Use an Expression transformation:-

Expression Transformation Interview Question ( Part - 2)
Expression Transformation Interview Question ( Part - 2)

Keep in mind the string length of the variable and output ports.
CUME function is used to calculate the cumulative amount based on the argument of the cumulative function. This means, if we call CUME with argument 1, e.g. CUME(1); then on the first call it will re-turn 1; on the second call, it will return 2; on the third call, it will return 3 and so on. Since Informatica process data row by row, this means that when the first row is processed CUME(1) will return 1; for the next row, it will return 2 and so on.

How can we implement aggregation operation without using an Aggregator Transformation in Informatica?

We will use the very basic concept of the Expression Transformation, that at a time we can access the previous row data as well as the currently processed data in an expression transformation. What we need is simple Sorter, Expression and Filter transformation to achieve aggregation at Informatica level.

Friday, 2 October 2015

Filter Transformation and why it is an Active one?

A Filter transformation is an Active and Connected transformation that can filter rows in a mapping.

Only the rows that meet the Filter Condition pass through the Filter transformation to the next transformation in the pipeline. TRUE and FALSE are the implicit return values from any filter condition we set. If the filter condition evaluates to NULL, the row is assumed to be FALSE. The numeric equivalent of FALSE is zero (0) and any non-zero value is the equivalent of TRUE.
Filter Transformation and why it is an Active one?
Filter Transformation and why it is an Active one?

As an ACTIVE transformation, the Filter transformation may change the number of rows passed through it. A filter condition returns TRUE or FALSE for each row that passes through the transformation, depending on whether a row meets the specified condition. Only rows that return TRUE pass through this transformation. Discarded rows do not appear in the session log or reject files.

Source Qualifier Transformations Filter Vs Filter Transformation

Source Qualifier Transformations Filter Vs Filter Transformation
Source Qualifier Transformations Filter Vs Filter Transformation

SQ Source Filter

Source Qualifier transformation filters rows when read from a source.

Source Qualifier transformation can only filter rows from relational sources.

Source Qualifier limits the row set extracted from a source.

Source Qualifier reduces the number of rows used throughout the mapping and hence it provides better performance.

The filter condition in the Source Qualifier transformation only uses standard SQL as it runs in the database.

Filter Transformation

Filter transformation filters rows from within a mapping

Filter transformation filters rows coming from any type of source system in the map-ping level.

Filter transformation limits the row set sent to a target.

To maximize session performance, include the Filter transformation as close to the sources in the mapping as possible to filter out unwanted data early in the flow of data from sources to targets.

Filter Transformation can define a condition using any statement or transformation function that returns either a TRUE or FALSE value.


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