Data Warehousing Interview Questions and Answers (2025)
Top Data Warehousing Interview Questions and Answers
Answer:
A data warehouse is a central repository of integrated data from multiple sources. It stores historical and current data to support business intelligence activities such as reporting, analysis, and decision-making.
Queries:
data
warehouse definition, data
warehouse in business intelligence
Answer:
· Source systems
· ETL (Extract, Transform, Load) tools
· Staging area
· Data storage
· Metadata
· OLAP engine
· Reporting tools
Queries:
data
warehouse architecture, ETL
in data warehousing
Answer:
|
OLTP |
OLAP |
|
Online Transaction Processing |
Online Analytical Processing |
|
Handles real-time transactions |
Handles complex queries and analysis |
|
Highly normalized |
De-normalized for faster querying |
OLTP vs OLAP,
data warehouse OLAP
Answer:
A star schema is a data modeling technique where a central fact table is connected to multiple dimension tables, forming a star-like structure.
Queries:
star schema
example, data warehouse
schema types
Answer:
A snowflake schema is a more normalized form of a star schema where dimension tables are split into additional tables. It reduces redundancy but may affect query performance.
Queries:
snowflake
schema, star vs snowflake
schema
Answer:
ETL stands for Extract, Transform, Load:
· Extract data from source systems
· Transform data into a suitable format
· Load it into the data warehouse
Queries:
ETL process,
data transformation in ETL
Answer:
· Fact Table: Contains measurable data (e.g., sales, revenue)
· Dimension Table: Contains descriptive attributes (e.g., date, customer)
Queries:
facts and
dimensions, fact table vs
dimension table
Answer:
SCD refers to how data warehouse systems manage and track changes in dimension data over time.
·Type 1: Overwrite old data
·Type 2: Add new row
·Type 3: Add new column
Queries:
slowly
changing dimension types, SCD
in data warehouse
Answer:
A data mart is a subset of a data warehouse, focused on a specific business line or department like sales or finance.
Queries:
data mart vs
data warehouse
Answer:
Data modeling defines how data is structured and stored, using schema designs such as star, snowflake, and galaxy schemas.
Queries:
data modeling
in data warehouse, schema
design
Answer:
Metadata is data about data. It helps manage, locate, and understand data in a warehouse.
Queries:
metadata
example, metadata types in
DW
Answer:
Data cleansing involves identifying and correcting errors or inconsistencies in data before loading it into a data warehouse.
Queries:
data
cleansing in ETL, data
quality management
Answer:
Popular ETL tools include:
· Informatica PowerCenter
· Talend
· Microsoft SSIS
· Apache NiFi
Queries:
ETL tools
comparison, best ETL tool
for data warehouse
Answer:
Data granularity refers to the level of detail in the data stored. Fine granularity means detailed data; coarse granularity means summarized data.
Queries:
granularity
in data warehouse
Answer:
A database supports real-time transactional data processing (OLTP), while a data warehouse supports historical data analysis (OLAP).
Queries:
data
warehouse vs database, difference
between database and data warehouse
Answer:
Conformed dimensions are consistent, reusable dimensions across multiple fact tables or data marts.
Queries:
conformed
dimension example
Answer:
Aggregate tables store pre-summarized data to speed up query performance.
Queries:
aggregate
fact table, pre-aggregated
data warehouse
Answer:
Junk dimensions combine low-cardinality flags and indicators into a single dimension to reduce clutter in the schema.
Queries:
junk
dimension example, dimension
optimization
Answer:
A surrogate key is an artificial primary key used in data warehouses instead of natural keys for better performance and stability.
Queries:
surrogate key
vs natural key, data
warehouse key management
Answer:
It involves validating data integrity, ETL workflows, and performance to ensure the accuracy and reliability of data in the warehouse.
Queries:
data
warehouse testing checklist, ETL
testing questions
Top Interview Questions and Answers on Datawarehousing ( 2025 )
1. What is a Data Warehouse?
- Answer: A Data Warehouse is a centralized repository that stores integrated data from multiple sources. It is designed for query and analysis rather than transaction processing, providing a platform for business intelligence activities. Data in a warehouse is structured, historical, and often organized in a way that optimizes reporting and analysis.
2. What are the main differences between OLTP and OLAP?
- Answer:
- OLTP (Online Transaction Processing) systems are designed for managing day-to-day transactional data. They focus on fast query processing and maintaining data integrity in multi-access environments. Examples of OLTP systems include banking applications and ERP systems.
- OLAP (Online Analytical Processing) systems, on the other hand, are designed for complex queries and large volumes of data analysis. They provide insights through data aggregation and multidimensional analysis. Typical OLAP applications include business reporting and data mining.
3. What is ETL?
- Answer: ETL stands for Extract, Transform, Load. It refers to the process of extracting data from various source systems, transforming the data into a suitable format or structure, and loading it into a data warehouse. ETL is crucial for ensuring that the data in the warehouse is accurate, consistent, and ready for analysis.
4. Explain the concepts of star schema and snowflake schema.
- Answer:
- Star Schema: A type of database schema that consists of a central fact table connected to several dimension tables. The architecture resembles a star, making it easier and faster to query and retrieve data.
- Snowflake Schema: A more complex schema that normalizes dimension tables into multiple related tables. While it saves storage space and reduces redundancy, it can increase the complexity of queries and reduce performance in some cases.
5. What is a Fact Table?
- Answer: A Fact Table is the central table in a data warehouse schema that contains quantitative data (metrics) for analysis. It stores facts (measurable, numerical data) and is typically denormalized. Fact tables often contain foreign keys that link to the dimension tables.
6. What is a Dimension Table?
- Answer: A Dimension Table contains descriptive attributes related to the facts in a fact table. They provide context to the data, allowing users to analyze facts by various metrics (dimensions). For example, a sales fact table might have dimensions such as time, product, and customer.
7. What are Slowly Changing Dimensions (SCD)?
- Answer: Slowly Changing Dimensions are dimensions that change over time in a data warehouse while still maintaining a history of changes. There are several types of SCD:
- Type 1: Overwrites old data with new data (no history).
- Type 2: Creates a new row with the new data, preserving the history.
- Type 3: Adds a new column to track changes (limited history).
8. What is data normalization and denormalization?
- Answer:
- Normalization is the process of organizing data to reduce redundancy and dependency by dividing a database into smaller, related tables. It adheres to certain normal forms to eliminate data anomalies.
- Denormalization is the process of deliberately introducing redundancy into a database by combining tables, which can enhance query performance in data warehousing.
9. What is data governance?
- Answer: Data governance refers to the overall management of the availability, usability, integrity, and security of the data employed in an organization. It encompasses policies, procedures, and standards to ensure proper data usage and compliance with regulations, promoting accountability and responsibility over data assets.
10. Can you explain the concept of data mart?
- Answer: A Data Mart is a smaller, more focused subset of a data warehouse, typically designed for a specific business line or team (e.g., sales, finance). Data marts enhance performance and reduce complexity by filtering down the vast amounts of data in a data warehouse to a more manageable size and scope.
11. What are some common data warehouse bottlenecks?
- Answer: Common bottlenecks may include:
- Inefficient ETL processes leading to slow data loading.
- Poorly designed schemas that complicate queries.
- Insufficient hardware resources (CPU, memory, storage) for data processing.
- Complex and unoptimized queries that slow down performance.
12. What is a surrogate key?
- Answer: A surrogate key is a unique identifier for an entity in a database that is created and managed by the database system rather than derived from business data. It is often implemented as an auto-incrementing integer, and it is used in a data warehouse to link fact and dimension tables while providing consistency.
Conclusion
These questions cover a wide array of fundamental concepts, processes, and architectures associated with data warehousing. Preparing for these questions can help candidates demonstrate their knowledge and skills effectively during an interview.
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