ETL (Extract, transform, load) Interview Questions And Answers

We can use advanced external transformation. for more detail you can refer the manual of informatica transformation guide in that advance external transformation. You can use c++ language on unix and c++, vb vc++ on windows server.

If you use PowerCenter, you can increase the number of partitions in a pipeline to improve session performance. Increasing the number of partitions allows the Informatica Server to create multiple connections to sources and process partitions of source data concurrently.

When you create a session, the Workflow Manager validates each pipeline in the mapping for partitioning. You can specify multiple partitions in a pipeline if the Informatica Server can maintain data consistency when it processes the partitioned data.

When you configure the partitioning information for a pipeline, you must specify a partition type at each partition point in the pipeline.
The partition type determines how the Informatica Server redistributes data across partition points.

The Workflow Manager allows you to specify the following partition types:

Round-robin partitioning. The Informatica Server distributes data evenly among all partitions. Use round-robin partitioning where you want each partition to process approximately the same number of rows.

For more information, see Round-Robin Partitioning.
Hash partitioning. The Informatica Server applies a hash function to a partition key to group data among partitions. If you select hash auto-keys, the Informatica Server uses all grouped or sorted ports as the partition key. If you select hash user keys, you specify a number of ports to form the partition key. Use hash partitioning where you want to ensure that the Informatica Server processes groups of rows
with the same partition key in the same partition. For more
information, see Hash Partitioning.

Key range partitioning. You specify one or more ports to form a compound partition key. The Informatica Server passes data to each partition depending on the ranges you specify for each port. Use key range partitioning where the sources or targets in the pipeline are partitioned by key range. For more information, see Key Range Partitioning.
Pass-through partitioning. The Informatica Server passes all rows at one partition point to the next partition point without redistributing them. Choose pass-through partitioning where you want to create an additional pipeline stage to improve performance, but do not want to change the distribution of data across partitions.

ETL Tool:

It is used to Extract(E) data from multiple source systems(like RDBMS,Flat files,Mainframes,SAP,XML etc) transform(T) them based on Business requirements and Load(L) in target locations.(like tables,files etc).

Need of ETL Tool:

An ETL tool is typically required when data scattered accross different systems.(like RDBMS,Flat files,Mainframes,SAP,XML etc).

Informatica Metadata is data about data which stores in Informatica repositories.

ETL is extraction , transforming , loading process , you will extract data from the source and apply the business role on it then you will load it in the target

the steps are :
1-define the source(create the odbc and the connection to the source DB)
2-define the target (create the odbc and the connection to the target DB)
3-create the mapping ( you will apply the business role here by adding transformations , and define how the data flow will go from the source to the target )
4-create the session (its a set of instruction that run the mapping , )
5-create the work flow (instruction that run the session)

Specify the Full path of the Shell script the "Post session properties
of session/workflow".

Informatica allows end users and partners to extend the metadata stored in the repository by associating information with individual objects in the repository. For example, when you create a mapping, you can store your contact information with the mapping. You associate information with repository metadata using metadata extensions.

Informatica Client applications can contain the following types of metadata extensions:

Vendor-defined. Third-party application vendors create vendor-defined metadata extensions. You can view and change the values of vendor-defined metadata extensions, but you cannot create, delete, or redefine them.
User-defined. You create user-defined metadata extensions using PowerCenter/PowerMart. You can create, edit, delete, and view user-defined metadata extensions. You can also change the values of user-defined extensions.

we cant directly import the xml file in informatica.
we have to define the microsoft excel odbc driver on our system. and define the name in exce sheet by defining ranges.
then in inforematica open the folder using sources ->import from database->select excel odbc driver->connect->select the excel sheet name .

Snapshots are read-only copies of a master table located on a remote node which is periodically refreshed to reflect changes made to the master table. Snapshots are mirror or replicas of tables.

Views are built using the columns from one or more tables. The Single Table View can be updated but the view with multi table cannot be updated.

A View can be updated/deleted/inserted if it has only one base table if the view is based on columns from one or more tables then insert, update and delete is not possible.

Materialized view
A pre-computed table comprising aggregated or joined data from fact and possibly dimension tables. Also known as a summary or aggregate table.

Yes,we can override a native sql query in source qualifier and lookup transformation.

In lookup transformation we can find "Sql override" in lookup properties.by using this option we can do this.

One foolproof method is to maintain a field called 'Last Extraction Date' and then impose a condition in the code saying 'current_extraction_date > last_extraction_date'.

First Method: If there is a column in the source which identifies the record inserted date. Then it will be easy to put a filter condition in the source qualifier.

Second Method: If there is no record in the source to identify the record inserted date. Then we need to do a target lookup based on the primary key and determine the new record and then insert.

Data staging is actually a collection of processes used to prepare source system data for loading a data warehouse. Staging includes the following steps:

Source data extraction, Data transformation (restructuring),

Data transformation (data cleansing, value transformations),

Surrogate key assignments

Full Load: completely erasing the contents of one or more tables and reloading with fresh data.

Incremental Load: applying ongoing changes to one or more tables based on a predefined schedule.

first time what we are loading the data is called initial load or full load.
secondtime or modified data waht ewe are loading is called as incremental load or delta load.

A mapping represents dataflow from sources to targets.
A mapplet creates or configures a set of transformations.

A workflow is a set of instruction sthat tell the Informatica server how to execute the tasks.

A worklet is an object that represents a set of tasks.

A session is a set of instructions that describe how and when to move data from sources to targets.

Full Load: completely erasing the contents of one or more tables and reloading with fresh data.

Incremental Load: applying ongoing changes to one or more tables based on a predefined schedule

Refresh Load: the table will be truncated and data will be loaded again. Here we use to load static dimension table or type tables using this method.
Incremental Load: It is a method to capture on the newly created or updated record. Based upon the falg or Date this load will be performed.
Full Load: when we are loading the data for first time, either it may be a base load or history all the set of records will be loaded at a strech depends upon the volume.

A data warehouse can be thought of as a three-tier system in which a middle system provides usable data in a secure way to end users. On either side of this middle system are the end users and the back-end data stores.

PowerCenter - ability to organize repositiries into a data mart domain and share metadata across repositiries.

PowerMart - only local repositiry can be created.

Additve: A masure can participate arithmatic calulatons using all or any demensions.

Ex: Sales profit

Semi additive: A masure can participate arithmatic calulatons using some demensions.

Ex: Sales amount

Non Additve:A masure can't participate arithmatic calulatons using demensions.

Ex: temparature

When addressing a table some dimension key must reflect the need for a record to get extracted. Mostly it will be from time dimension (e.g. date >= 1st of current mth) or a transaction flag (e.g. Order Invoiced Stat). Foolproof would be adding an archive flag to record which gets reset when record changes.

1. Connected lookup

2. Unconnected lookup

Connected lookup will receive input from the pipeline and sends output to the pipeline and can return any number of values.it does not contain retun port.

Unconnected lookup can return only one column. it containn return port.

he best procedure to take a help of debugger where we monitor each and every process of mappings and how data is loading based on conditions breaks.

Explain your self

ETL - The process of extracting data from multiple sources.(ex. flat files,XML, COBOL, SAP etc) is more simpler with the help of tools.
Manual - Loading the data other than flat files and oracle table need more effort.

ETL - High and clear visibilty of logic.
Manual - complex and not so user friendly visibilty of logic.

ETL - Contains Meta data and changes can be done easily.
Manual - No Meta data concept and changes needs more effort.

ETL- Error hadling,log summary and load progess makes life easier for developer and maintainer.
Manual - need maximum effort from maintainance point of view.

ETL - Can handle Historic data very well.
Manual - as data grows the processing time degrads.

These are some differences b/w manual and ETL developement.

The ANALYZE statement allows you to validate and compute statistics for an index, table, or cluster. These statistics are used by the cost-based optimizer when it calculates the most efficient plan for retrieval. In addition to its role in statement optimization, ANALYZE also helps in validating object structures and in managing space in your system. You can choose the following operations: COMPUTER, ESTIMATE, and DELETE. Early version of Oracle7 produced unpredicatable results when the ESTIMATE operation was used. It is best to compute
your statistics.

EX:

select OWNER,
sum(decode(nvl(NUM_ROWS,9999), 9999,0,1)) analyzed,
sum(decode(nvl(NUM_ROWS,9999), 9999,1,0)) not_analyzed,
count(TABLE_NAME) total
from dba_tables
where OWNER not in ('SYS', 'SYSTEM')
group by OWNER

You can verify the session log and ensure the source fetched and target loaded details