Data Warehouse Interview Questions and Answers

1. What is Data Warehousing?

Data Warehousing is the process of collecting, storing, and managing large volumes of data from different sources to provide meaningful business insights. It supports analytical reporting, structured and ad hoc queries, and decision-making processes.

2. What are the key components of a Data Warehouse?

Data Source
Data Staging Area
Data Storage
Presentation Layer
Metadata
Management and Control

3. What is ETL in Data Warehousing?

ETL stands for Extract, Transform, Load. It is a process that extracts data from source systems, transforms it into a suitable format, and loads it into a data warehouse.

4. What is the difference between OLAP and OLTP?

OLAP (Online Analytical Processing) is used for analytical and business intelligence purposes.
OLTP (Online Transaction Processing) is used for day-to-day transaction processing.

5. What is a Star Schema?

Star Schema is a type of data warehouse schema that organizes data into fact and dimension tables. The fact table is at the center, and dimension tables are connected to it, forming a star-like structure.

6. What is a Snowflake Schema?

Snowflake Schema is a more normalized form of Star Schema where dimension tables are split into sub-dimension tables. It reduces redundancy and improves data integrity.

7. What is a Fact Table?

A Fact Table is the central table in a star or snowflake schema that contains quantitative data for analysis and is linked to dimension tables.

8. What is a Dimension Table?

A Dimension Table contains attributes related to the dimensions along which facts can be analyzed. These are descriptive fields like time, geography, product, etc.

9. What are the types of Fact Tables?

Transactional Fact Table
Snapshot Fact Table
Accumulating Fact Table

10. What is Data Mart?

A Data Mart is a subset of a data warehouse focused on a specific business area or department, like sales, finance, or marketing.

11. What is a Surrogate Key?

A Surrogate Key is a unique identifier for each record in a table, typically used instead of natural primary keys. It is often a sequential number.

12. What is Data Granularity?

Data Granularity refers to the level of detail or depth of the data stored in the data warehouse. Higher granularity means more detailed data.

13. What is Slowly Changing Dimension (SCD)?

SCD refers to how data warehouses manage and track changes in dimension data over time. There are several types: Type 1 (overwrite), Type 2 (add new row), and Type 3 (add new column).

14. What is Metadata?

Metadata is data about data. In data warehousing, it describes the structure, operations, and contents of the data warehouse system.

15. What is a Data Lake?

A Data Lake is a storage repository that can hold a vast amount of raw data in its native format until needed. Unlike a data warehouse, it stores structured, semi-structured, and unstructured data.

16. What is the difference between Data Warehouse and Data Lake?

Data Warehouse stores structured data optimized for analytics.
Data Lake stores all types of data and is used for big data analytics and machine learning.

17. What is the role of a Data Warehouse in Business Intelligence?

A Data Warehouse acts as the central repository that consolidates data from various sources and supports business intelligence tools in generating reports and dashboards.

18. What are Conformed Dimensions?

Conformed Dimensions are dimensions that are consistent and can be used across multiple fact tables or data marts in a data warehouse.

19. What is a Factless Fact Table?

A Factless Fact Table is a fact table that does not contain measurable data (facts) but captures events or relationships among dimensions.

20. What is a Junk Dimension?

A Junk Dimension is a structure that combines several low-cardinality flags and indicators into a single dimension table to reduce clutter in the fact table.

21. What is the difference between Normalization and Denormalization?

Normalization reduces data redundancy and improves data integrity by organizing data into multiple related tables.
Denormalization improves query performance by combining tables to reduce the number of joins.

22. What is a Data Warehouse Architecture?

Data Warehouse Architecture is the design and structure of the data warehouse system. Common types include single-tier, two-tier, and three-tier architectures.

23. What are the challenges in Data Warehousing?

Data Quality
Data Integration
Scalability
Performance
Security

24. What is Data Profiling?

Data Profiling is the process of examining the data available in a data source and collecting statistics and information about that data to understand its structure, quality, and content.

25. What is a Staging Area in a Data Warehouse?

A Staging Area is an intermediate storage area used for data processing during the ETL process. It temporarily holds data extracted from source systems before it is transformed and loaded into the data warehouse.

26. What is the difference between a primary key and a surrogate key in a Data Warehouse?

Primary Key is derived from the source system and represents natural identifiers.
Surrogate Key is a system-generated unique key used in data warehouse tables for easier data management and tracking.

27. What is Data Cleansing?

Data Cleansing is the process of identifying and correcting or removing inaccurate, incomplete, or irrelevant data from a dataset to improve data quality.

28. What are some common ETL tools used in Data Warehousing?

Informatica PowerCenter
Microsoft SQL Server Integration Services (SSIS)
Talend
Apache Nifi
IBM DataStage

29. What is Data Integration?

Data Integration involves combining data from different sources and providing users with a unified view of the data for analysis and reporting.

30. What is the Bus Schema?

Bus Schema is a design where standardized dimensions are shared across fact tables using conformed dimensions, which simplifies integration across different business processes.

31. What is the difference between batch processing and real-time processing in ETL?

Batch Processing involves processing data in bulk at scheduled intervals.
Real-Time Processing involves processing data immediately as it becomes available.

32. What is Data Lineage?

Data Lineage describes the life cycle of data, showing where data originates, how it moves through systems, how it is transformed, and where it is used.

33. What is a Dashboard in Business Intelligence?

A Dashboard is a visual interface that displays key metrics and data insights using charts, graphs, and KPIs to help decision-makers monitor business performance.

34. What is a Data Cube?

A Data Cube is a multi-dimensional array of values typically used to describe data in OLAP systems for complex querying and analysis.

35. What is Drill-Down and Roll-Up in OLAP?

Drill-Down allows users to view more detailed data.
Roll-Up summarizes the data to show a higher level of aggregation.

36. What are Aggregate Tables?

Aggregate Tables store pre-computed summarized data to improve query performance by reducing the amount of data scanned.

37. What is the difference between a Fact Table and Dimension Table?

Fact Table contains measurable metrics or facts.
Dimension Table contains descriptive attributes related to facts.

38. What is a Mini-Dimension?

A Mini-Dimension is a subset of a larger dimension table, often used to handle rapidly changing attributes by storing frequently changing data in a separate, smaller table.

39. What are Slowly Changing Dimensions (SCD) Types?

Type 0 – Passive
Type 1 – Overwrite
Type 2 – Add new row
Type 3 – Add new column
Type 4 – History table
Type 6 – Hybrid

40. What is the role of a Data Warehouse Developer?

A Data Warehouse Developer is responsible for designing, developing, and maintaining data warehouse systems, including writing ETL scripts, optimizing queries, and ensuring data integrity and performance.

41. What is the difference between schema-on-write and schema-on-read?

Schema-on-write validates and structures data before storing (used in Data Warehouses).
Schema-on-read applies schema when data is read (used in Data Lakes).

42. What is a Data Governance in Data Warehousing?

Data Governance refers to the policies, standards, and procedures that ensure the quality, integrity, security, and proper usage of data within the data warehouse system.

43. What is an Operational Data Store (ODS)?

ODS is a database designed to integrate data from multiple sources for additional operations on the data before it is moved to a data warehouse.

44. What are the advantages of a Data Warehouse?

Improved business intelligence
Better decision making
Historical analysis
Data consistency and quality

45. What are the disadvantages of a Data Warehouse?

High implementation cost
Complexity in design and maintenance
Long development time
Difficulty in handling unstructured data

46. What is a Logical Data Model?

A Logical Data Model defines the structure of the data elements and relationships between them without considering how they will be physically implemented.

47. What is a Physical Data Model?

A Physical Data Model includes all table structures, including column data types, constraints, indexes, and relationships, tailored to a specific database system.

48. What is Real-Time Data Warehousing?

Real-Time Data Warehousing involves the process of loading and analyzing data in the data warehouse in real-time, enabling instant insights and actions.

49. What is a Hybrid Data Warehouse?

A Hybrid Data Warehouse combines both on-premise and cloud-based systems to balance performance, flexibility, and cost-efficiency.

50. What are some best practices for Data Warehousing?

Clearly define business requirements
Ensure high data quality and consistency
Implement robust data governance policies
Use scalable architecture
Monitor performance and optimize regularly

Request more information

Data Warehouse Interview Questions and Answers