A Data Warehouse, also called a centralised, is a collection point for large amounts of data from different resources designed to manage large data sets in today’s tech-driven society.

Nowadays, it has grown increasingly popular and sought-after among data management specialists.

This blog presents a complete set of Data Warehouse interview questions and answers to OLAP and SCIMAS topics, covering everything from architecture (WAP RULAP Database, etc.)

This resource will equip you to answer even the most challenging Data Warehouse questions, whether experienced in general terms and concepts, complex methods, or Data Warehouse techniques. It covers it all.

Each question on this blog has been carefully created to help you gain knowledge and understand Data Warehouse concepts and applications, building your confidence for any interview preparation or Data Warehouse exam.

So, let’s dive in to Data Warehouse interview questions!

1. What remains data warehousing, and what are its main components?

Data warehousing collects, cleans, integrates, and manages data to provide meaningful insights for informed decision-making.

Its main components include OLAP (Online Analytical Processing), dimensions, facts, and measures.

2. What is OLAP, and how does it work in a Data Warehouse?

OLAP technology enables users to quickly and selectively extract and manipulate data from a database to analyse it.

It runs queries on the Data Warehouse, allowing users to gain insights from the consolidated data.

3. Why is fixing inaccurate records in the ETL process important?

Fixing inaccurate records in the ETL process is essential to ensure the validity and reliability of reports, as invalid data can lead to incorrect conclusions and decisions.

4. Can you explain the difference between a Data Warehouse and a database?

A Data Warehouse is designed for analytical needs and stores large amounts of data in a central location for efficient access and analysis.

A database, on the other hand, is typically used for transaction processing and day-to-day operations.

The data in a Data Warehouse is more processed and informative, while the data in a database is rawer and more operational.

5. What is the pivot operation in an OLA Data Warehouse?

The pivot operation, also known as the rotation operation, transposes both the access (x and y-axis) to provide an alternative presentation of data.

In this operation, the location dimension on the y-axis and the item dimension on the x-axis are transposed, resulting in the item dimension coming over the location dimension and the location dimension coming over the item dimension.

6. What is SCIMAS, and what is its role in data warehousing?

SCIMAS likely refers to the “Scientific Information Management and Analysis System,” a tool or method used in data warehousing to manage and analyse large volumes of scientific data.

It facilitates efficient data storage, retrieval, and analysis for research and decision-making purposes.

7. What is the purpose of data warehousing architecture?

Data warehousing architecture aims to convert raw data from various sources into meaningful information and store it efficiently and insightfully for retrieval and analysis.

This involves various activities, such as data extraction, cleaning, transformation, and loading.

8. How does a Data Warehouse differ from a database regarding stored data?

A Data Warehouse stores processed data, metadata, and aggregate data for analytical purposes, while a database stores raw data primarily for transaction processing.

9. Explain the role of the Extract, Transform, and Load (ETL) process in a Data Warehouse.

The ETL process transfers data from various sources, such as databases or flat files, to a temporary storage area called the staging area and eventually into the Data Warehouse.

This process ensures the data is clean, consistent, and in the correct format for efficient retrieval and analysis.

10. What is OLAP, and how does it differ from OLTP?

OLAP stands for online analytical processing and is used for analysing and manipulating multidimensional data.

It differs from OLTP (online transaction processing) as OLTP manages and processes day-to-day transactions.

OLAP allows for seeing data from multiple angles, views, and dimensions, leading to new ideas and insights.

11. What are the three main data components stored in a Data Warehouse?

The three main components of data stored in a Data Warehouse are raw data, metadata, and aggregate data.

Raw data is the transferred data, metadata provides information about the data, and aggregate data contains information about the tables, their attributes, and the data type of each attribute.

12. What is online analytical processing (WAP)?

WAP refers to running queries on the Data Warehouse for analysis purposes.

An analysis-based method allows end users to gain insights from the consolidated data.

13. What are data marks in a Data Warehouse, and how do they contribute to security?

Data marks are separate from user groups and provide extra security in Data Warehouse systems.

Each user group has limited access to the Data Warehouse, and the purpose of human data is to restrict access to the entire organisation’s data.

14. What remains the structure of the start schema in a warehouse?

In the start schema, every dimension table is linked to the fact table, and fact tables are the centre, containing keys to every dimension table and attributes like units sold and revenue.

15. What is the no-flex schema in a warehouse?

The no-flex schema is a slightly modified version of the start schema, which is normalised by splitting it into additional tables.

16. How does data warehousing facilitate efficient data management and security?

Data warehousing divides a Data Warehouse into smaller parts, allowing different users or groups to access only the relevant information they need.

17. What is the role of OLAP cubes in data warehousing?

OLAP cubes process multidimensional data for more detailed and efficient analysis.

They store data in a multifaceted form, offering multiple dimensions and views of the same data.

In contrast, OLAP pure refers to a single view of all the data related to sales and different products.

18. What are the advantages of OLAP over OLTP?

These include seeing data from multiple angles and different views and dimensions, supporting activities like filtering and sorting data, and handling refined data that is easier to read and provides more information from raw data.

19. What are the limitations of MOLAP in handling data?

MOLAP has limitations in handling a large amount of data.

It can only take a limited amount of data at a time.

20. What are the compensations of RULAP over MOLAP?

RULAPs include handling a large amount of data simultaneously and running multidimensional analyses and queries on the same database.

21. What is hybrid OLAP, and what are its advantages?

Hybrid OLAP combines MOLAP and RULAP.

Its advantage is the ability to drill through from the cube into the underlying relational data, using the best features of both multi-dimensional OLAP and relational OLAP.

22. Can you explain the differences between RULAP, multi-dimensional OLAP, and hybrid OLAP in data management?

RULAP, multi-dimensional OLAP, and hybrid OLAP are different approaches to data management.

RULAP allows more processing time and disk space, while hybrid OLAP combines both features.

23. What is roll-up in a data cube operation?

Roll-up is a data cube operation that aggregates dimensions by climbing up a concept hierarchy for a dimension or reducing a dimension.

For example, it converts the dimension of cities into a country dimension by adding the attributes of the cities from the USA and Canada.

24. Explain the concept of drill-down operation in a data cube.

A drill-down operation is the reverse of a roll-up operation.

In this operation, we can break down the time dimension into three different attributes, each of which would be further divided into three attributes, summing up to 12.

25. Can you provide an example of roll-up and drill-down operations?

For instance, we aggregated the set of attributes into more minor attributes by converting the dimension of cities into a country dimension.

We then did a drill-down operation on the time dimension, represented by quarters one, three, and four.

We broke down the quarter into different months, creating 12 attributes in total.

25. Can you explain the difference between OLAP and OLAP pure?

The difference between OLAP and OLAP pure is that OLAP cubes store data in a multidimensional form.

26. What is the difference between roll-up and drill-down operations?

A roll-up operation aggregates the attributes and reduces the number of characteristics in a dimension.

In contrast, a drill-down operation breaks down the attributes, increasing the number of factors in a dimension.

27. Explain the concept of operations in an OLA Data Warehouse.

Operations in an OLA Data Warehouse refers to different ways of manipulating and analysing data.

The three primary operations are slice, dice, and pivot.

These operations allow users to extract, segment, and present data differently to gain insights and visualise data more efficiently.

28. What are the data dimensions in a Data Warehouse?

Data dimensions are tables that describe the dimensions involved in a Data Warehouse.

They are similar to tables in a database, with different dimensions describing different aspects of the Data Warehouse.

A customer dimension contains customer details like customer ID, name, and address, while a product dimension has attributes like ID, name, and type.

A date dimension includes the order, shipment, and delivery dates.

29. What is the significance of having different dimensions in a Data Warehouse project?

Having different dimensions in a Data Warehouse questions and answers project is beneficial because it allows end users to query these dimensions, efficiently providing descriptive information.

As a result, end users can get the answers they need without searching multiple sizes, making the analysis and reporting process more efficient.

30. What are the operations in OLAP and their significance in data warehousing?

The operations in OLAP include rolling up, drilling down, slicing, dicing, and pivoting.

31. Which data typesare contained in a fact table in a Data Warehouse?

A fact table in a Data Warehouse contains two types of data dimension keys and a measure.

The dimension critical links to the dimension table, while the measure calculates the data in the dimension.

Any arithmetic operation performed should be stored as a measure in the fact table.

32. What is the role of a fact table in a Data Warehouse?

A fact table plays a crucial role in a Data Warehouse as it is necessary for any query or analysis on any dimension.

Every dimension table in the Data Warehouse must have a fact table, allowing for querying and analysis flexibility.

Storing the results of arithmetic operations and measures in the fact table makes it easier to perform various queries, sorting, and drill-down operations.

33. What is the relationship between dimension and fact tables in a Data Warehouse?

Every dimension table is linked to a fact table in a Data Warehouse.

Each dimension has a corresponding fact table, allowing for more querying and analysis flexibility.

The dimension key in the fact table connects to the dimension, and any data it receives, such as addition, subtraction, average, summing, or manipulation, is stored as a measure in the fact table.

34. Can you explain the three types of OLAP cubes?

The three types of OLAP cubes are MOLAP (multi-dimensional online analytical processing), ROLAP (relational online analytical processing), and whole-up (hybrid online analytical processing).

MOLAP processes and shows data directly into a multi-dimensional database.

35. How do dimensions, facts, and measures help manage a Data Warehouse?

Dimensions, facts, and measures are crucial in Data Warehouse management.

It helps structure the data, perform various queries, and analyse it more efficiently.

Dimensions provide descriptive information, while facts and measures allow for summing, averaging, or manipulating data.

This approach helps gain insights and make informed decisions about the Data Warehouse.

36. What is a schema in a Data Warehouse, and why is it important?

A schema in a Data Warehouse refers to the structure or organisation of data, including tables, relationships, and database objects.

It is essential because it helps understand the dimensions, facts, and measures necessary for efficient Data Warehouse management.

A well-designed schema ensures that the data is organised to support querying and analysis, making it easier for end users to access and utilise the data.

37. What five operations can be performed using OLA activities in a Data Warehouse?

The five operations that can be performed using OLA activities in a Data Warehouse are slice, dice, pivot, roll-up, and pivot.

38. Why are facts and measures essential for Data Warehouse management?

Facts and measures are essential for Data Warehouse management because they provide flexibility and enable efficient data collection and analysis.

Understanding these concepts is crucial for effective and efficient Data Warehouse management.

39. What is a schema in database management, and why is it essential in a Data Warehouse?

A schema in database management is a logical description of the entire database, providing details on constraints placed on tables, fundamental values present, and relationships between different tables.

It is essential in a Data Warehouse because it helps maintain data integrity, prevent duplicate values, and ensure data is organised and analysed efficiently.

40. What schemas are used in a Data Warehouse to establish relationships?

The three types of schemas used in a Data Warehouse to establish relationships are task schema, snowflake schema, and fact constellation schema.

41. What is the zeroth slice operation in an OLA Data Warehouse?

The zeroth slice operation in an OLA Data Warehouse creates a new sub-cube from one particular dimension in a given cube.

If we have a cube with three dimensions, we can use the zeroth slice operation to break it down into two sizes and a cube.

In the example provided, the z-axis is missing, so the time dimension is taken as a time slice, resulting in different representations for different quarters.

42. How is the relationship between the fact and dimension table established in a star schema?

A star schema establishes the relationship between the fact and dimension table using the dealer ID as the primary key.

The dealer ID is the foreign key in the fact table.

43. What are the essential foreign keys in a Data Warehouse, and why are they crucial?

Primary essential foreign keys in a Data Warehouse are unique identifiers that ensure each dimension table can have only one entry, and each primary key cannot have duplicate values.

They are crucial for maintaining data integrity and preventing the same values.

44. How are duplicate values achieved in a fact table in a Data Warehouse?

Duplicate values can be achieved in a fact table using the foreign key, which references the dimension table and looks up to it.

This allows the same values to be reflected in all four tables.

45. What are the two data types in a fact table in a Data Warehouse?

The two data types in a fact table are dimension keys and measures.

46. What is the dice operation in an OLA Data Warehouse?

The dice operation in an OLA Data Warehouse provides a new sub-cube from two or more dimensions in a given cube.

For instance, Toronto and Vancouver’s location, time, and item dimensions can be determined.

The time dimension can be diced for quarter one or quarter two, while the item dimension can be chopped for mobile or modem devices, ignoring mobile and modem phones.

47. What remains a dimension key in a warehouse?

A dimension key is a foreign key that references the primary key in the dimension table.

48. What is a measure in a fact table in data Architecture?

A measure is the calculation or arithmetic value based on the operation performed on the dimensions.

49. How many dimensions and additional tables are present in the dealer table in the star schema?

In a star schema, the dealer table has only one dimension table and two additional tables concerning location and country.

50. How is the snowflake schema different from the star schema?

In the snowflake schema, the dealer table is split into two additional tables concerning location and country, creating a snowflake schema that is further normalised into other tables.

51. What is the galaxy schema in Warehouse?

The galaxy schema, also known as the fact conservation schema, has more than one fact table, with additional dimension tables normalised in the snowflake schema.

52. What are the two primary functions of a Data Warehouse?

A Data Warehouse’s primary functions are maintaining past and present records and helping organisations make effective business decisions through precise data analysis.

53. What are some key features of data warehousing?

Some key data warehousing features include subject-oriented analysis, integration, non-volatile Data Warehouses, and time-variant Data Warehouses.

54. What is metadata in data warehousing?

Metadata is a critical feature in data warehousing that answers all data-related questions in the Data Warehouse.

It is also known as the table of contents for the data, data catalogue, data directory, Data Warehouse roadmap, and nerve centre.

55. What is online analytical processing (OLAP)?

Online analytical processing (OLAP) is a computational technique that allows users to analyse multidimensional data from multiple perspectives interactively.

56. What is the purpose of dimensional models in Data Warehouses?

Dimensional models allow users to store and analyse information on each dimension, enabling data analysis across multiple dimensions, up to the peer attribute, down to the child attribute, and through data from an OLAP cube with a relational database.

57. What is the primary difference between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing)?

The primary difference between OLTP and OLAP is that OLTP focuses on small transactions and fast query processes, while OLAP often involves complex queries involving aggregations.

58. What is the architecture of a Data Warehouse?

A Data Warehouse’s architecture consists of two main components: an interface designed from operational systems and an individual Data Warehouse design.

59. What are the layers in the Data Warehouse architecture?

The layers in the Data Warehouse architecture include the data source layer, the extraction layer, the staging area, and the target Data Warehouse.

60. What is the purpose of a Data Warehouse?

The primary purpose of a Data Warehouse is to attain cleansed, integrated, and adequately aligned data so that it is easy to analyse and present to clients and customers in several businesses.

61. In what industries are Data Warehouses commonly used?

Data Warehouses are used in various sectors, such as the airline industry, banking, healthcare, public sector, investment and insurance, retail chains, telecommunication, and the hospitality industry.

62. Name some data warehousing tools available in the market.

Some data warehousing tools available in the market include Oracle, Amazon Redshift, and Mark Logic.

63. What does Oracle offer in terms of Data Warehouse solutions?

Oracle offers a wide range of Data Warehouse solutions for both on-premise and cloud, helping to optimise customer experience and increase operational efficiency.

64. What are some data warehousing solutions that make data more accessible and faster?

Some data warehousing solutions that make data more accessible and faster include Mark Logic and Oracle.

65. According to B-scale, what is the annual compensation range for Data Warehouse professionals?

The annual compensation of Data Warehouse professionals ranges from $68,000 to $149,000, with the median salary being about $100,000 per annum.

66. What is the purpose of a Data Warehouse system?

A Data Warehouse system is designed to store transaction data from multiple databases like M S SQL, KLSI, and BB2—tables and schemas in a Data Warehouse store transaction data.

67. Why are databases designed to maintain a limited number of transactions?

Databases are designed to maintain a limited number of transactions because they help to avoid losing all the transactions if a user has made a significant number of transactions.

68. What are some benefits and challenges of using data warehousing tools like Oracle, Amazon Redshift, and Mark Logic?

The benefits of using data warehousing tools include optimising customer experience, increasing operational efficiency, and streamlining data management processes.

However, the challenges involve considering each user’s specific needs and requirements and the potential impact on the overall performance of the Data Warehouse system.

69. What is the limitation of generating reports based on OLTP data?

The limitation of generating reports based on OLTP data is that it only contains recent data, making it insufficient for making informed business decisions.

70. What is the consequence of maintaining only recent data in a database?

Maintaining only recent data in a database means that queries will take longer to execute, negatively impacting the customer experience.

71. What is historical data, and where is it stored?

Historical data refers to all the data from day one.

In the given text, historical data is stored in the second database.

72. When is the first record inserted into the first and second databases?

The first record is inserted into the first database, while the exact first record is inserted into the second database when the user meets the third transaction.

73. What is the purpose of storing historical data in a Data Warehouse?

Storing historical data in a Data Warehouse involves analysing and makingbusiness decisions.

74. What is the difference between the first-dayand second databasesregarding historical transactions?

The first-day database is not considered a historical transaction database because it only has recent transactions.

In contrast, the second database contains all recorded transactions.

75. What is MSSCEL, and how is it used to store historical data?

MSSCEL is a database management system; tables store historical data.

76. What is the first VLTP database, and how is its data stored?

The first VLTP database contains recent online transaction (OLTP) data.

This data is stored in the warehouse with historical data from day one.

77. What is the choice between maintaining recent or historical data?

Maintaining recent or historical data depends on the user’s needs and requirements.

78. Why is a Data Warehouse valuable database for businesses?

A Data Warehouse database is helpful for businesses because it contains years of transaction data that can be analysed to make informed decisions about operations and maintain profitability.

79. Why is historical data necessary for making informed business decisions?

Historical data is necessary for making informed business decisions because it provides a comprehensive view of business operations and helps companies understand past trends, which can be used to predict future outcomes.

80. What tools maintain data between the OLTP and the Data Warehouse?

Tools such as Informatica Data Stage, Warehouse Builder, and P L S Q L maintain data between the OLTP and the Data Warehouse.

81. Who maintains the data between the OLTP and the Data Warehouse?

Leading ETL developers are responsible for maintaining the data between the OLTP and the Data Warehouse.

82. What is an ODS database, and why is it created?

An ODS (Operational Data Store) database is created to address the issue of decreased database performance due to heavy usage by millions of customers accessing raw quantity details or performing online transactions.

It has schemas within the same database, with one schema for OLTP and another for other applications, each with different layers to maintain the data separately.

83. What potential issues may arise during the ETL process?

Some potential issues during the ETL process include duplicate rows in the OLTP database and incorrect or invalid Data Warehouse data.

84. What are the two main schema models used in a Data Warehouse?

The star and snowflake schemas are the two main schema models used in a Data Warehouse.

85. What is the primary difference between the star and snowflake schema models?

The primary difference between the star and snowflake schema models is the number of dimension keys in the fact table.

In the star schema, all dimension keys are in the fact table, while in the snowflake schema, only three are.

86. What is the role of an architect in designing a data model for a Data Warehouse?

An architect’s role in designing a data model for a Data Warehouse is to create a table structure that can be easily accessed for report generation.

They decide which columns should be in a dimension table and which should be in a fact table.

87. How do dimension and fact tables store and analyse data in a Data Warehouse?

Dimension tables are designed to hold non-measurable data, while fact tables contain measurable data.

They store and analyse data in a Data Warehouse, allowing for efficient data management and reporting.

88. What are the four types of dimensions in a Data Warehouse?

The four dimensions in a Data Warehouse are conformed, juncture, degenerated, and slowly changing dimensions.

89. What is a conform dimension, and what is an example?

A conformed dimension is data that can be reused across multiple projects.

An example is an employee or calendar dimension.

A Data Warehouse may seem complex, but you can build one successfully with proper knowledge and preparation.

This Data Warehouse interview questions blog covers everything from basic concepts and interview questions on Data Warehouse testingterminologies to more in-depth strategies and processes, equipping you to quickly answer even the most challenging interview questions.

Understanding and applying Data Warehouse concepts and applications will boost your confidence for interviews and job success.

Data Warehouse interview questions for experienced persons and data management professionals will become even more vital as data management becomes increasingly necessary, as willData Warehouse architect interview questions.

Any industry’s success depends on constant learning and adaptation, including Data Warehouse developments.

Stay abreast of updates to this field by regularly practising blog interview questions, and then work hard toward becoming a Data Warehouse specialist! This can also help with Data Warehouse viva questions.

Thank you for exploring Data Warehouse interview questions with us, including the Data Warehouse design interview questions.

Our best wishes go out to the ever-evolving Data Warehouse sector!