Datastage Interview Questions and Answers V1.3

Datastage Interview Related Questions and Answers V1.3

You will get Answers for below mentioned questions, in this Post: 

# Details of Data partitioning and collecting methods in Datastage?

# Remove duplicates using Sort Stage and Remove Duplicate Stages and Diffrence?

# What is difference between Copy & transformer stage ?

# What is the use Enterprise Pivot Stage ?

# What are Stage Variables, Derivations and Constants? 

# What is the difference between change capture and change apply stages?

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# Details of Data partitioning and collecting methods in Datastage?

Partitioning mechanism divides a portion of data into smaller segments, which is then processed independently by each node in parallel. It helps make a benefit of parallel architectures like SMP, MPP, Grid computing and Clusters. 

1. Auto
2. Same
3. Round robin
4. Hash
5. Entire
6. Random
7. Range
8. Modulus

Collecting is the opposite of partitioning and can be defined as a process of bringing back data partitions into a single sequential stream (one data partition).

1.      Auto

2.      Round Robin

3.      Ordered

4.      Sort Merge

 ** Data partitioning methods : Datastage supports a few types of Data partitioning methods which can be implemented in parallel stages:

  Auto - default. Datastage Enterprise Edition decides between using Same or Round Robin partitioning. Typically Same partitioning is used between two parallel stages and round robin is used between a sequential and an EE stage.

  Same - existing partitioning remains unchanged. No data is moved between nodes.

  Round robin - rows are alternated evenly accross partitions. This partitioning method guarantees an exact load balance (the same number of rows processed) between nodes and is very fast.

  Hash - rows with same key column (or multiple columns) go to the same partition. Hash is very often used and sometimes improves performance, however it is important to have in mind that hash partitioning does not guarantee load balance and misuse may lead to skew data and poor performance.

  Entire - all rows from a dataset are distributed to each partition. Duplicated rows are stored and the data volume is significantly increased.

  Random - rows are randomly distributed accross partitions

  Range - an expensive refinement to hash partitioning. It is imilar to hash but partition mapping is user-determined and partitions are ordered. Rows are distributed according to the values in one or more key fields, using a range map (the 'Write Range Map' stage needs to be used to create it). Range partitioning requires processing the data twice which makes it hard to find a reason for using it.

  Modulus - data is partitioned on one specified numeric field by calculating modulus against number of partitions. Not used very often.

** Data collecting methods : A collector combines partitions into a single sequential stream. Datastage Parallel supports the following collecting algorithms:

  Auto - the default algorithm reads rows from a partition as soon as they are ready. This may lead to producing different row orders in different runs with identical data. The execution is non-deterministic.

  Round Robin - picks rows from input partition patiently, for instance: first row from partition 0, next from partition 1, even if other partitions can produce rows faster than partition 1.

  Ordered - reads all rows from first partition, then second partition, then third and so on.

  Sort Merge - produces a globally sorted sequential stream from within partition sorted rows. Sort Merge produces a non-deterministic on un-keyed columns sorted sequential stream using the following algorithm: always pick the partition that produces the row with the smallest key value.

#Remove duplicates using Sort Stage and Remove Duplicate Stages and Diffrence?

We can remove duplicates using both stages but in the sort stage we can capture duplicate records using create key change column property.

1)The advantage of using sort stage over remove duplicate stage is that sort stage allows us to capture the duplicate records whereas remove duplicate stage does not.
2) Using a sort stage we can only retain the first record.
Normally we go for retaining last when we sort a particular field in ascending order and try to get the last rec. The same can be done using sort stage by sorting in descending order to retain the first record.

#What is difference between Copy & transformer stage ?

In a copy stage there are no constraints or derivation so it surely should perform better than a transformer. If you want a copy of a dataset you better use the copy stage and if there any business rules to be applied to the dataset you better use the transformer stage.

We use the copy stage to change the metadata of input dataset(like changing the column name)

# What is the use Enterprise Pivot Stage ?

The Pivot Enterprise stage is a processing stage that pivots data horizontally and vertically.

  Specifying a horizontal pivot operation : Use the Pivot Enterprise stage to horizontally pivot data to map sets of input columns onto single output columns.

Table 1. Input data for a simple horizontal pivot operation
REPID	last_name		Jan_sales		Feb_sales		Mar_sales
100	Smith		1234.08		1456.80		1578.00
101	Yamada		1245.20		1765.00		1934.22
Table 2. Output data for a simple horizontal pivot operation
REPID		last_name		Q1sales		Pivot_index
100		Smith		1234.08		0
100		Smith		1456.80		1
100		Smith		1578.00		2
101		Yamada		1245.20		0
101		Yamada		1765.00		1
101		Yamada		1934.22		2

 Specifying a vertical pivot operation: Use the Pivot Enterprise stage to vertically pivot data and then map the resulting columns onto the output columns.

Table 1. Input data for vertical pivot operation
REPID	last_name		Q_sales
100	Smith		1234.08
100	Smith		1456.80
100	Smith		1578.00
101	Yamada		1245.20
101	Yamada		1765.00
101	Yamada		1934.22
Table 2. Out put data for vertical pivot operation
REPID	last_name	Q_sales (January)		Q_sales1 (February)	Q_sales2 (March)	Q_sales_average
100	Smith	1234.08		1456.80	1578.00	1412.96
101	Yamada	1245.20		1765.00	1934.22	1648.14

 

# What are Stage Variables, Derivations and Constants? 

Stage Variable - An intermediate processing variable that retains value during read and doesnt pass the value into target column.

Derivation - Expression that specifies value to be passed onto the target 

Constraint- is like a filter condition which limits the number of records coming from input according to business rule.

The right order is : Stage variables Then Constraints Then Derivations

#What is the difference between change capture and change apply stages?

Change capture stage is used to get the difference between two sources i.e. after dataset and before dataset. The source which is used as a reference to capture the changes is called after dataset. The source in which we are looking for the change is called before dataset. This change capture will add one field called "chage code" in the output from this stage. By this change code one can recognize which kind of change this is like whether it is delete, insert or update.
 

Change apply stage is used along with change capture stage. It takes change code from the change capture stage and apply all the changes in the before dataset based on the change code.

 

Change Capture is used to capture the changes between the two sources.
 

Change Apply will apply those changes in the output file.