1) What is ETL?

In data-warehousing architecture, ETL is an important component, which manages the data for any business process. ETL stands for Extract, Transform and Load.  Extract does the process of reading data from a database.  Transform does the converting of data into a format that could be appropriate for reporting and analysis. While, load does the process of writing the data into the target database.

2) Explain what are the ETL testing operations includes?

ETL testing includes

  • Verify whether the data is transformed 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

3) 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

  • Info Processing
  • Analytical Processing
  • Data Mining

Data mining can be defined 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

4) What are the various tools used in ETL?

  •  Cognos Decision Stream
  • Oracle Warehouse Builder
  • Business Objects XI
  • SAS business warehouse
  • SAS Enterprise ETL server

5) What is a 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

- Additive Facts

- Semi-additive Facts

- Non-additive Facts

6) 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 multi-dimensional form for reporting purposes.  It consists of facts called as measures categorized by dimensions.

7) 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.


8) Explain what is Grain of Fact?

Grain fact can be defined as the level at which the factual information is stored.  It is also known as Fact Granularity

9) Explain what factless fact schema is and what is Measured?

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 the employee count in a company.

The numeric data based on columns in a fact table is known as Measures

10) Explain what is transformation?

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

11) Explain the use of Lookup Transformation?

The Lookup Transformation is useful for

  • Getting a related value from a table using a column value
  • The Update slowly changing dimension table
  • Verify whether records already exist in the table

12) Explain what is partitioning, hash partitioning and round robin partitioning?

To improve performance, transactions are subdivided, this is called as Partitioning.  Partitioning enables Informatica Server for creating 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 Partitioning 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 process groups of rows with the same partitioning key in the same partition need to be ensured

13) Mention what is the advantage of using the 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 another application can consume the data.

Learn more about ETL Testing Interview Questions in this blog post.

Interested in mastering ETL Testing?

Check out this blog post to learn more ETL Testing Tutorials

14) Using SSIS ( SQL Server Integration Service) what are the possible ways to update the table?

To update the table using SSIS the possible ways are:

  • Use a SQL command
  • Use a staging table
  • Use Cache
  • Use the Script Task
  • Use full database name for updating if MSSQL is used

15) In case you have a 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 a source

16) 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 the flat files Static cache is used

17) Explain what are the differences between Unconnected and Connected lookup?


18) Explain what is the 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.

19)  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 database with some process of cleansing data.

Example: Data stage, Informatica etc.

While OLAP is meant for reporting purpose on OLAP data available in the multi-directional model.

Example: Business Objects, Cognos etc.

20) 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 gateway. The next step is to generate the ABAP code for the  mapping,then only informatics can pull data from SAP
  • To connect and import sources from external systems Power Connect is used

21) Mention what is the difference between Power Mart and Power Center?


22) Explain what staging area is and what is the purpose of a staging area?

Data staging is an area where you hold the data temporarily on the data warehouse server. Data staging includes the following steps

  • Source data extraction and data transformation ( restructuring )
  • Data transformation (data cleansing, value transformation )
  • Surrogate key assignments

23) What is Bus Schema?

In the various business process to identify the common dimensions, BUS schema is used.  It comes with a conformed dimension along with a standardized definition of information

24) Explain what is data purging?

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

25) Explain what are Schema Objects?

Schema objects are the logical structure that directly refers to the database data.  Schema objects includes tables, views, sequence synonyms, indexes, clusters, function packages and database links

26) Explain these terms Session, Worklet, Mapplet and Workflow ?

  • Mapplet : It arranges or creates sets of transforms
  • 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

27) List few ETL bugs.

Calculation Bug, User Interface Bug, Source Bugs, Load condition bug, ECP related bug.