ETL Testing Interview Questions

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Q. What is ETL?
In data warehousing architecture, ETL is an important component, which manages the data for any business process. ETL refers to Extracting, Transforming and Loading of Data from any outside system to the required place. These are the basic 3 steps in the Data Integration process. Extracting means locating the Data and removing from the source file, transforming is the process of transporting it to the required target file and Loading the file in the target system in the format applicable.

Q. Explain what are the ETL testing operations includes?
ETL testing includes
Verify whether the data is transforming correctly according to business requirements
Verify that the projected data is loaded into the data warehouse without any truncation and data loss
Make sure that ETL application reports invalid data and replaces with default values
Make sure that data loads at expected time frame to improve scalability and performance

Q. Why ETL testing is required?
To keep a check on the Data which are being transferred from one system to the other.
To keep a track on the efficiency and speed of the process.
To be well acquainted with the ETL process before it gets implemented into your business and production.

Q. What are ETL tester responsibilities?
Requires in depth knowledge on the ETL tools and processes.
Needs to write the SQL queries for the various given scenarios during the testing phase.
Should be able to carry our different types of tests such as Primary Key, defaults and keep a check on the other functionality of the ETL process.
Quality Check

Q. What are Dimensions?
Dimensions are the groups or categories through which the summarized data are sorted.

Q. What is Staging area referring to?
Staging area is the place where the data is stored temporarily in the process of Data Integration. Here, the data s cleansed and checked for any duplication.

Q. Explain ETL Mapping Sheets.
ETL mapping sheets contains all the required information from the source file including all the rows and columns. This sheet helps the experts in writing the SQL queries for the ETL tools testing.

Q. Mention few Test cases and explain them.
Mapping Doc Validation – Verifying if the ETL information is provided in the Mapping Doc.
Data Check – Every aspect regarding the Data such as Data check, Number Check, Null check are tested in this case
Correctness Issues – Misspelled Data, Inaccurate data and null data are tested.

Q. List few ETL bugs.
Calculation Bug, User Interface Bug, Source Bugs, Load condition bug, ECP related bug.
In addition to the above ETL testing questions, there may be other vital questions where you may be asked to mention the ETL tools which you have used earlier. Also, you might be asked about any debugging issues you have faced in your earlier experience or about any real time experience.

Q. Mention what are the types of data warehouse applications and what is the difference between data mining and data warehousing?
The types of data warehouse applications are
1. Info Processing
2. Analytical Processing
3. Data Mining
Data mining can be define as the process of extracting hidden predictive information from large databases and interpret the data while data warehousing may make use of a data mine for analytical processing of the data in a faster way. Data warehousing is the process of aggregating data from multiple sources into one common repository

Q. What are the various tools used in ETL?
1. Cognos Decision Stream
2. Oracle Warehouse Builder
3. Business Objects XI
4. SAS business warehouse
5. SAS Enterprise ETL server

Q. What is fact? What are the types of facts?
It is a central component of a multi-dimensional model which contains the measures to be analysed.  Facts are related to dimensions.
Types of facts are
1. Additive Facts
2. Semi-additive Facts
3. Non-additive Facts

Q. Explain what are Cubes and OLAP Cubes?
Cubes are data processing units comprised of fact tables and dimensions from the data warehouse. It provides multi-dimensional analysis.
OLAP stands for Online Analytics Processing, and OLAP cube stores large data in muti-dimensional form for reporting purposes.  It consists of facts called as measures categorized by dimensions.

Q. Explain what is tracing level and what are the types?
Tracing level is the amount of data stored in the log files.  Tracing level can be classified in two Normal and Verbose. Normal level explains the tracing level in a detailed manner while verbose explains the tracing levels at each and every row.

Q. Explain what is Grain of Fact?
Grain fact can be defined as the level at which the fact information is stored.  It is also known as Fact Granularity

Q. Explain what factless fact schema is and what is Measures?
A fact table without measures is known as Factless fact table.  It can view the number of occurring events. For example, it is used to record an event such as employee count in a company.
The numeric data based on columns in a fact table is known as Measures

Q. Explain what is transformation?
A transformation is a repository object which generates, modifies or passes data.  Transformation are of two types Active and Passive

Q. Explain the use of Lookup Transformation?
The Lookup Transformation is useful for
Getting a related value from a table using a column value
Update slowly changing dimension table
Verify whether records already exist in the table

Q. Explain what is partitioning, hash partitioning and round robin partitioning?
To improve performance, transactions are sub divided, this is called as Partitioning.  Partioning enables Informatica Server for creationg of multiple connection to various sources
The types of partitions are
Round-Robin Partitioning:
By informatica data is distributed evenly among all partitions
In each partition where the number of rows to process are approximately same this partioning is applicable
Hash Partitioning:
For the purpose of partitioning keys to group data among partitions Informatica server applies a hash function
It is used when ensuring the processes groups of rows with the same partitioning key in the same partition need to be ensured

Q. Mention what is the advantage of using DataReader Destination Adapter?
The advantage of using the DataReader Destination Adapter is that it populates an ADO recordset (consist of records and columns) in memory and exposes the data from the DataFlow task by implementing the DataReader interface, so that other application can consume the data.

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Q. Using SSIS ( SQL Server Integration Service) what are the possible ways to update table?
To update table using SSIS the possible ways are:
1. Use a SQL command
2. Use a staging table
3. Use Cache
4. Use the Script Task
5. Use full database name for updating if MSSQL is used

Q. In case you have non-OLEDB (Object Linking and Embedding Database) source for the lookup what would you do?
In case if you have non-OLEBD source for the lookup then you have to use Cache to load data and use it as source

Q. In what case do you use dynamic cache and static cache in connected and unconnected transformations?
Dynamic cache is used when you have to update master table and slowly changing dimensions (SCD) type 1
For flat files Static cache is used

Q. Explain what are the differences between Unconnected and Connected lookup?

Connected Lookup Unconnected Lookup
Connected lookup participates in mapping It is used when lookup function is used instead of an expression transformation while mapping
Multiple values can be returned Only returns one output port
It can be connected to another transformations and returns a value Another transformation cannot be connected
Static or dynamic cache can be used for connected Lookup Unconnected as only static cache
Connected lookup supports user defined default values Unconnected look up does not support user defined default values
In Connected Lookup multiple column can be return from the same row or insert into dynamic lookup cache Unconnected lookup designate one return port and returns one column from each row

Q. Explain what is data source view?
A data source view allows to define the relational schema which will be used in the analysis services databases.  Rather than directly from data source objects, dimensions and cubes are created from data source views.

Q. Explain what is the difference between OLAP tools and ETL tools?
The difference between ETL and OLAP tool is that
ETL tool is meant for the extraction of data from the legacy systems and load into specified data base with some process of cleansing data.
Example: Data stage, Informatica etc.
While OLAP is meant for reporting purpose in OLAP data available in multi-directional model.
Example: Business Objects, Cognos etc.

Q. How you can extract SAP data using Informatica?
With the power connect option you extract SAP data using informatica
Install and configure the PowerConnect tool
Import the source into the Source Analyzer.  Between Informatica and SAP Powerconnect act as a gateaway. The next step is to generate the ABAP code for the mapping then only informatica can pull data from SAP
To connect and import sources from external systems Power Connect is used

Q. Mention what is the difference between Power Mart and Power Center?

Power Center Power Mart
Suppose to process huge volume of data Suppose to process low volume of data
It supports ERP sources such as SAP, people soft etc. It does not support ERP sources
It supports local and global repository It supports local repository
It converts local into global repository It has no specification to convert local into global repository

Q. Explain what staging area is and what is the purpose of a staging area?
Data staging is an area where you hold the data temporary on data warehouse server.  Data staging includes following steps
Source data extraction and data transformation ( restructuring )
Data transformation (data cleansing, value transformation )
Surrogate key assignments

Q. What is Bus Schema?
For the various business process to identify the common dimensions, BUS schema is used.  It comes with a conformed dimensions along with a standardized definition of information

Q. Explain what is data purging?
Data purging is a process of deleting data from data warehouse. It deletes junk data’s like rows with null values or extra spaces.

Q. Explain what are Schema Objects?
Schema objects are the logical structure that directly refer to the databases data.  Schema objects includes tables, views, sequence synonyms, indexes, clusters, functions packages and database links

Q. Explain these terms Session, Worklet, Mapplet and Workflow?
Mapplet : It arranges or creates sets of transformation
Worklet: It represents a specific set of tasks given
Workflow: It’s a set of instructions that tell the server how to execute tasks
Session: It is a set of parameters that tells the server how to move data from sources to target


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