2023's Top Data Warehouse Interview Questions - IQCode Experts

What is Data Warehouse?

Data warehousing is a process of collecting and analyzing data from various sources to derive valuable business insights. It involves integrating and analyzing data from multiple sources for the creation of a single data repository. This data repository is used as the central part of the business intelligence system and aids in the analysis and reporting on data.

The data warehouse comprises a collection of technology and components that help the organization with data strategy. It is an electronic storage system intended for inquiry and analysis of large volumes of data. The system converts the data into useful information that is made available to aid decision-making.

The process of creating a data warehouse involves cleaning, integrating, and consolidating data. The warehouse is separate from the operational database of the company. Its architecture gives users access to current and historical decision-support data that is difficult to find in the standard operational data store.

The data warehouse provides a novel design that can improve query performance and minimize response time for reporting and analytics. Such systems are also known as Decision Support System (DSS), Management Information System, Executive Information System, Analytic Application, or Business Intelligence Solution.

Data Warehouse Interview Questions for Freshers

1. What is Data Mining? How is it different from Data Warehousing?

Understanding OLAP in Data Warehousing

OLAP stands for Online Analytical Processing and it refers to the technology used for analyzing data in a multi-dimensional format. OLAP is an important component of data warehousing as it allows analysts to gain insights into the data and make informed decisions.

When selecting an OLAP system, there are several guidelines that should be followed:

1. Scalability: The OLAP system must be able to handle large volumes of data and support multiple users simultaneously.

2. Flexibility: The system must be flexible enough to allow analysts to slice and dice the data in different ways to gain insights.

3. Performance: The system must be able to provide quick response times when querying the data.

4. Security: The system must have robust security features to prevent unauthorized access to sensitive data.

5. Interoperability: The system should integrate easily with other tools and technologies used in the organization.

By following these guidelines, organizations can select an OLAP system that meets their requirements and helps them to make better decisions based on their data.

Understanding Fact Tables in the Context of Data Warehouses

In the context of a data warehouse, a fact table is a central table that stores quantitative data about a particular business process or event. It contains the measurements, metrics, or facts that represent the key performance indicators (KPIs) of a business.

There are different types of fact tables, including transactional fact tables, periodic snapshot fact tables, and accumulating snapshot fact tables. Transactional fact tables contain detailed data about specific business transactions, while periodic snapshot fact tables capture metrics at specified intervals, such as daily, weekly, or monthly. Accumulating snapshot fact tables track the state of a process as it progresses over time.

Properly designing fact tables is crucial to ensure efficient querying and analysis of data in data warehouses. It is essential to select relevant dimensions, create appropriate granular levels, and avoid redundancy or unnecessary data.


In the context of data warehousing, a dimension table is a table that contains textual and descriptive data related to the facts in a fact table. It is used to categorize and store data according to different dimensions such as time, location, product, and customer.

Advantages of using a dimension table include:

1. Improved query performance: By using a dimension table, the query performance can be improved significantly, as the data is already categorized and normalized based on relevant dimensions. 2. Better data quality: As dimension table stores textual and descriptive data, it helps to ensure that the information is accurate, consistent, and reliable. 3. Simplified database design: Dimension tables help to simplify the database design by reducing the need for multiple joins between tables, which can be complex and time-consuming. 4. Enhanced business analysis: By grouping data into dimensions, it becomes easier for businesses to analyze the data, identify trends, and make informed decisions.

Code: N/A

Different Types of Dimension Tables in Data Warehousing

In data warehousing, there are different types of dimension tables that hold descriptive data to provide context to the measurements recorded in the fact table.

Some of the commonly used types of dimension tables include:

  • Slowly Changing Dimensions (SCD) - to maintain historical information about dimensions that change over time
  • Junk Dimensions - to store low cardinality attributes
  • Conformed Dimensions - to share a common dimension among multiple fact tables
  • Role-Playing Dimensions - to provide different perspectives of a dimension to a fact table
  • Factless Fact Tables - to support many-to-many relationships between dimensions

By using these different types of dimension tables, data professionals can better organize and analyze data in a data warehouse.

Difference between Fact Table and Dimension Table

In data warehousing, Fact Table and Dimension Table are two types of tables used to organize data efficiently. The main difference between them is the type of data they contain and their relationship with each other. Here are the key differences:

  • Fact Table: It contains the quantitative data of a business process, such as sales figures, revenue, or quantities. It usually has a large number of records but fewer columns. Fact tables don't contain descriptive attributes.
  • Dimension Table: It contains descriptive data about the business process, such as time, date, location, or customer. It has a smaller number of records but many columns. Dimension tables contain attributes that describe the data in the fact table.

In summary, Fact tables and Dimension tables work together in a data warehouse to provide relevant information and analysis. Fact tables contain raw data, while Dimension tables provide context to the raw data so that it can be interpreted meaningfully.

//Example of Fact table
   sales_id INTEGER,
   product_id INTEGER,
   date_id INTEGER,
   quantity INTEGER,
   revenue DECIMAL(10, 2)
//Example of Dimension table
CREATE TABLE date_dim (
   date_id INTEGER,
   date DATETIME,
   year INTEGER,
   month INTEGER,
   day INTEGER,
   week INTEGER,
   quarter INTEGER,
   holiday VARCHAR(50)

Advantages of a Data Warehouse

A data warehouse provides numerous advantages including:

- Improved data quality: Data is gathered from different sources and transformed to consistent formats, resulting in high-quality data for decision making.

- Integrated view of data: Data from multiple sources is integrated into a single view, allowing for more efficient analysis and reporting.

- Enhanced decision making: Business intelligence tools provide insights and analysis to support data-driven decision making.

- Increased organizational efficiency: A data warehouse reduces the need for repetitive data gathering and processing.

- Scalability: Data warehouses can handle large amounts of data, allowing organizations to easily store and analyze data as the business grows.

- Cost savings: By removing the need for manual data processing, businesses can save time and money.

Disadvantages of Using a Data Warehouse

While there are certainly benefits to using a data warehouse, there are also some potential drawbacks to consider:

  • Cost: Implementing a data warehouse can be expensive, both in terms of money and resources.
  • Complexity: Designing, building, and maintaining a data warehouse can be complex and require specialized skills.
  • Data quality: A data warehouse is only as good as the data that is inputted into it, so ensuring data quality is essential.
  • Newer technologies: The speed of technological advancement means that newer technologies are constantly being developed, making it hard to stay current.
  • Security: Storing large amounts of sensitive data in one place can pose security risks if not managed properly.
  • User adoption: If users are not comfortable using the data warehouse or do not understand how to use it properly, they may not find it useful.

Types of Data Warehouses

In general, there are three types of data warehouses - enterprise data warehouse (EDW), operational data store (ODS), and data mart.

An Enterprise Data Warehouse (EDW) consolidates data from various sources across an entire organization. It provides a single comprehensive view, supporting decision-making processes across departments.

An Operational Data Store (ODS) integrates data from multiple transaction-oriented systems in real-time. Unlike the EDW which is designed for reporting and analysis, the ODS is intended for operational processes.

A Data Mart serves a particular function, department, or user group within a company and contains a subset of the enterprise data warehouse. It is designed to support a specific business function such as sales, marketing, or finance.

Each of these types of data warehouses serves a specific purpose and caters to different business needs.

Types of Data Marts in Data Warehousing

In the context of data warehousing, there are three different types of data marts: dependent, independent, and hybrid.

Dependent data marts rely on the main data warehouse for their data, while independent data marts source their data from external sources. Hybrid data marts use a combination of both approaches.

Which type of data mart to use depends on the specific needs and requirements of the organization. Dependent data marts may be more cost-efficient and easier to maintain, while independent data marts may offer more flexibility and autonomy. Hybrid data marts may offer the best of both worlds, but may also be more complex to manage.

Difference between Data Warehouse and Database

A database is a collection of data that is organized and managed in a structured manner to enable efficient data manipulation and retrieval. Data is stored in tables and accessed using queries and commands.

A data warehouse, on the other hand, is a large repository of integrated data that is used for analysis and reporting. It is designed to support decision-making processes by providing a comprehensive and unified view of an organization's data from multiple sources.

The main differences between a database and a data warehouse are:

  1. Purpose: While a database is used to manage daily operations of an organization, a data warehouse is used for strategic decision-making.
  2. Data Structure: A database is designed to support transactional processing and store current, up-to-date data, while a data warehouse is designed to store historical data over a long-term period.
  3. Data Integration: A database contains data from a single source, while a data warehouse integrates data from multiple sources to provide a comprehensive view of an organization's data.
  4. Query Optimization: A database is optimized for transactional processing, while a data warehouse is optimized for complex queries and data analysis.

In summary, a database is used for day-to-day operations, while a data warehouse is used for reporting and analytical purposes.

// Example Code

// Database connection const db = mysql.createConnection({ host: "localhost", user: "username", password: "password", database: "mydatabase" });

// Data warehouse connection const dw = new DataWarehouse({ host: "localhost", user: "username", password: "password", database: "mydatawarehouse" });

// Example query to retrieve data from database const query = "SELECT * FROM customers WHERE country='USA'"; db.query(query, (err, result) => { if (err) throw err; console.log(result); });

// Example query to retrieve data from data warehouse const query = "SELECT * FROM sales WHERE year=2020 AND region='West'"; dw.query(query, (err, result) => { if (err) throw err; console.log(result); });

Understanding Factless Fact Table in Data Warehousing

A factless fact table is a type of table in a data warehouse that captures the relationship between two or more dimensions, without recording any measurable or quantitative data. It contains only foreign keys that refer to the primary keys of the dimensional tables.

For instance, a factless fact table can be used to indicate the occurrence of a particular event, such as the enrollment of a student in a course. The table would contain the foreign keys of the student and the course dimensions, but no statistical data.

Factless fact tables are useful in data warehouse design, especially when dealing with complex data relationships that cannot be expressed by a traditional fact table. They enable the tracking of multiple events and non-events without creating redundant or unnecessary data records, thus improving query performance.

Real-time Data Warehousing: Definition and Explanation

Real-time data warehousing refers to a process of extracting, transforming, and loading (ETL) data from various sources into a data warehouse in real-time or near real-time. This means that the data in the warehouse is constantly updated with the latest information from the sources.

The goal of real-time data warehousing is to provide timely, accurate, and relevant information for decision-making and analysis. This is especially important in industries where time-sensitive decisions are required, such as finance, healthcare, and manufacturing.

Real-time data warehousing requires specialized tools and techniques for ETL, data integration, and data quality management. It also requires a scalable and flexible architecture that can handle large volumes of data and process it quickly.

Overall, real-time data warehousing is a critical component of modern data-driven organizations, as it enables them to stay competitive and make informed decisions based on up-to-date information.

Active Data Warehousing - Definition

Active Data Warehousing refers to the process of continuously updating a data warehouse with real-time data from various sources, making the warehouse a more current and accurate representation of an organization's operations. Instead of relying on periodic batch updates, active data warehousing allows for immediate access to the most up-to-date information.

Characteristics of a Data Warehouse

A data warehouse is a large centralized repository of data that is used to support decision-making processes. Some of the key characteristics of a data warehouse are:

  • Subject-oriented: A data warehouse is organized around specific subject areas, such as sales, inventory, or customer information.
  • Integrated: Data from various sources are collected and combined into a single, unified view that eliminates data redundancies and inconsistencies.
  • Time-variant: A data warehouse stores historical data over a long period of time, allowing for analysis of trends and changes over time.
  • Non-volatile: A data warehouse is designed for inquiry and analysis rather than transaction processing, meaning that data is not changed or updated once it is stored in the data warehouse.
  • Reliable: A data warehouse is designed to ensure data accuracy and consistency through data validation, cleaning, and transformation processes.
  • Accessible: A data warehouse is typically accessed through business intelligence tools, such as data visualization and reporting tools, that allow users to query and analyze data.

Understanding Metadata and Its Purpose

Metadata is essentially data that describes other data. It provides information about a particular piece of data that helps to identify and understand it better. This information can include data such as the author, date created, file type, location, and more.

Metadata is used for various purposes such as organizing and categorizing data, improving search results, and providing context about the data. It helps users to easily discover, access, and understand the data, especially when dealing with large amounts of data. Additionally, metadata can be used for data management, preservation, and transfer. It is an essential part of digital content creation and management, which allows for efficient and effective use of data.

Some Popular Data Warehouse Solutions Used in the Industry

There are several data warehouse solutions available in the market. Some popular ones are:

- Amazon Redshift<br>
- Microsoft Azure SQL Data Warehouse<br>
- Google BigQuery<br>
- Snowflake<br>
- Oracle Autonomous Data Warehouse<br>
- IBM Db2 Warehouse<br>
- Cloudera Data Warehouse<br>
- SAP HANA<br>

List of Popular ETL Tools in the Industry:

Extract, Transform, Load (ETL) is a critical process in data warehousing and business intelligence. Here are some popular ETL tools used in the industry:

- Apache NiFi
- Talend
- Informatica PowerCenter
- IBM InfoSphere DataStage
- Microsoft SQL Server Integration Services (SSIS)
- Oracle Data Integrator (ODI)
- Pentaho Data Integration
- SAP Data Services
- Matillion ETL
- CloverDX

Understanding Star Schema in Data Warehousing

In data warehousing, a star schema is a type of database schema. It consists of a fact table that is connected to one or more dimension tables. The fact table contains numerical data, while the dimension tables contain descriptive data related to the fact data. The naming convention "star schema" comes from the fact that the diagram of this type of schema looks like a star, with the fact table in the center and the dimension tables surrounding it like the arms of a star. This structure is commonly used in data warehousing because it allows for efficient and fast querying of the data, making it easier for analysts to extract insights and make better business decisions.

Snowflake Schema in Data Warehousing

The snowflake schema is a model used in data warehousing that involves the normalization of dimension tables in a manner that resembles snowflakes, as opposed to traditional star schema models. This means that the dimension tables are further divided into sub-dimension tables. The result is a more complex and normalized schema that allows for more efficient use of storage space and query performance. It is often used in scenarios where there are many-to-one relationships between dimension tables. Overall, the snowflake schema is a commonly used schema in data warehousing that helps in the organization and management of data.

Understanding Data Cubes in Data Warehousing

In the context of data warehousing, a data cube represents a multi-dimensional dataset that allows for efficient analysis and processing of large volumes of data. It is a method of organizing and representing data in a way that permits relationships and patterns to be easily identified and analyzed by users. The data cube consists of dimensions, which are the categories by which the data can be sliced and diced, and measures, which are the numerical values that can be aggregated and analyzed. The data cube provides a powerful tool for decision-making, data mining, and data analysis in various industries, including finance, retail, and healthcare.

Architecture of a Data Warehouse

A data warehouse is a system used for storing and managing large volumes of data collected from multiple sources. It typically uses a three-tier architecture consisting of the following layers:

1. Bottom Tier: This layer is responsible for storing data from a variety of sources such as internal databases, external sources like web services, Excel spreadsheets, and CSV files.

2. Middle Tier: This layer is responsible for processing and transforming data loaded into the bottom tier, including data cleaning, aggregation, and summarization. The transformed data is then stored in a format suited for analysis and reporting.

3. Top Tier: This layer is used for querying and analyzing the data available in the middle tier. Business analysts and decision-makers typically access the data stored in this layer using dashboards, reports, and analytics tools.

Overall, the architecture of a data warehouse is designed to facilitate the efficient extraction of insights from large volumes of data collected from disparate sources.

Advantages and Disadvantages of the Top-Down Approach in Data Warehouse Architecture

The top-down approach in data warehouse architecture is a method that starts with the most general aspects of the business and drills down into the specific details. It has both advantages and disadvantages:


  • It is a more organized and structured approach to building a data warehouse, allowing for better management of the process.
  • It creates a structured and standardized view of the data, making it easier to understand and use for decision-making.
  • It ensures consistency in data across all departments, thus reducing errors and improving overall data accuracy.
  • It allows for better scalability and adaptability as the data warehouse can be expanded upon as the organization grows and changes.


  • It may take longer to implement compared to other approaches due to its comprehensive nature.
  • It may be difficult to obtain support and buy-in from all stakeholders due to the high level of planning and coordination required.
  • It may not be flexible enough to accommodate changes or additions to the data warehouse structure.
  • It may also require a higher level of technical expertise and resources to implement and maintain.
Note: It is important to carefully evaluate and consider the specific needs and requirements of an organization before deciding on the appropriate data warehouse architecture approach.

Advantages and Disadvantages of Bottom-Up Approach in Data Warehouse Architecture

In the bottom-up approach, the data warehouse is built by integrating data marts with each other. Here are some advantages and disadvantages of the bottom-up approach:


  • It allows for quick iterations and development of the data warehouse as it is built in a modular way.
  • It is easier to implement and manage as compared to the top-down approach
  • The approach prioritizes business needs and requirements and ensures relevance to end-users.
  • The approach can handle more complex queries and analysis as multiple data marts can be integrated together.


  • There is a higher risk of data redundancy as each data mart is built independently.
  • It may lead to inconsistency in data definitions and naming conventions among the data marts.
  • It may be difficult to maintain a consistent performance level as the data warehouse grows and more data marts are integrated.
  • The approach may not work well for organizations with a very large amount of data to analyze as it can lead to multiple data marts, making it difficult to manage and maintain.

Overall, the bottom-up approach can be a highly effective method for organizations with specific business requirements and a limited amount of data. However, larger organizations with a lot of data may want to consider a top-down approach instead.

Differences between Data Warehouse and Data Mart

A data warehouse is a large, centralized repository of data that is used to support business intelligence activities such as reporting, data exploration, and analysis. It contains data from a variety of sources and is designed to support enterprise-wide decision making.

On the other hand, a data mart is a subset of a data warehouse. It is a smaller repository of data that is designed to serve a specific business unit or department. Data marts typically contain data that is relevant to a particular set of users and their specific needs.

The main differences between the two are:

  • Scope: Data warehouses are enterprise-wide, while data marts are department-specific.
  • Size: Data warehouses are much larger than data marts.
  • Complexity: Data warehouses are more complex to design and maintain than data marts.
  • Usage: Data warehouses are used for enterprise-wide reporting and analysis, while data marts are used for department-level reporting and analysis.
//Sample code for creating a data warehouse 

CREATE TABLE customer_dim ( 

    customer_id INT PRIMARY KEY, 

    first_name VARCHAR(50), 

    last_name VARCHAR(50), 

    email VARCHAR(100), 

    creation_date DATE 


CREATE TABLE product_dim ( 

    product_id INT PRIMARY KEY, 

    name VARCHAR(100), 

    category VARCHAR(50), 

    price DECIMAL(10,2), 

    creation_date DATE 


CREATE TABLE sales_fact ( 

    sale_id INT PRIMARY KEY, 

    customer_id INT REFERENCES customer_dim (customer_id), 

    product_id INT REFERENCES product_dim (product_id), 

    date DATE, 

    quantity INT, 

    amount DECIMAL(10,2) 


Definition of Data Purging in Data Warehousing

Data purging in the context of data warehousing refers to the process of permanently deleting or removing data from a data warehouse. This process is typically done to improve the performance of the data warehouse, by freeing up storage space and reducing the amount of data that needs to be processed during queries. Data purging is often done in conjunction with data archiving, which involves moving older, less frequently accessed data to a separate storage location, rather than permanently deleting it.

Understanding Dimensional Modelling in Data Warehousing

Dimensional modelling is a technique used in data warehousing, which involves organizing data into a schema that is optimized for querying and analysis. It is a design approach that focuses on creating a logical and easy-to-understand structure for presenting data to users.

In dimensional modelling, data is organized into two types of tables: fact tables and dimension tables. Fact tables store quantitative data that can be aggregated, such as sales figures or customer orders. Dimension tables provide descriptive information about the facts, such as the time period, product type or customer demographic.

The design of dimensional models is based on the concepts of star schema or snowflake schema, which help simplify data querying by eliminating redundancies and improving the overall performance of the system. By using dimensional modelling, businesses can easily access data for reporting, analysis, and decision-making.

Data Lake vs Data Warehouse in the Context of Data Warehousing

Data Lake: A data lake is a large, centralized repository that allows storage of both structured and unstructured data at any size, shape or speed in its native format. It can hold enormous amounts of data, which can be processed and analyzed at a lower cost. Data lakes are an ideal choice for storing raw data that needs to be persisted and processed.

Data Warehouse: A data warehouse is a large, central repository intended for storing and managing data from varied sources. Unlike the data lake, the data warehouse is designed for delivering high-quality data for business analysis. Data is first extracted from different sources, transformed into a structure that can be easily queried and then loaded into the data warehouse.

Differences: The main difference between a data lake and data warehouse is in their design and processing mechanism. A data lake holds raw data in its original format, while a data warehouse stores processed data in a structured format. Data lakes offer more flexibility and scalability, while data warehouses are more suited for business intelligence purposes.

Differences Between Star Schema and Snowflake Schema in Data Warehousing

In data warehousing, the two popular approaches for organizing data are Star Schema and Snowflake Schema.

Star Schema:

It is a type of schema where a centralized fact table references multiple dimension tables. The schema takes the shape of a star, where the fact table is at the center, and the dimension tables surround it. The Star Schema is characterized by its simplicity, as all data is stored in a single table, reducing the number of joins necessary for analysis. It is used in applications where simple queries and fast aggregations are necessary.

Snowflake Schema:

The Snowflake Schema is an extension of the Star Schema, which means it is also dimensional-based. However, it features normalization of the dimension tables, meaning that the data is more structured, requiring more tables and more joins to query. The Snowflake Schema looks like a snowflake, where the fact table is still at the center, and the dimension tables branch out into more tables.

The main difference between the two schemas is that Star Schema is simpler, easier to understand and analyze, while Snowflake Schema offers more complex structures and allow for more precise data analysis at the cost of increased complexity.

Differentiating Between Agglomerative Hierarchical Clustering and Divisive Clustering

Agglomerative Hierarchical Clustering and Divisive Clustering are two different approaches to cluster analysis. The main difference between these two methods lies in the way the clusters are formed.

Agglomerative Hierarchical Clustering takes a bottom-up approach. In this method, each observation starts as its own cluster and then the algorithm recursively merges the pairs of clusters that are most similar, until all the observations belong to the same cluster.

On the other hand, Divisive Clustering follows a top-down approach. It starts with all observations in one cluster and then recursively splits the cluster into smaller ones until each observation is in its own cluster.

Therefore, the primary difference between these clustering methods is the order in which the clusters are formed - either by merging similar data points or by splitting the existing cluster.

Advantages of a Cloud-Based Data Warehouse

A cloud-based data warehouse offers several advantages over traditional on-premises data warehouses. Firstly, it provides greater scalability, which allows businesses to use as much or as little storage as needed, and can easily expand or downsize their storage as required. This results in reduced costs and better utilization of resources.

Secondly, cloud-based data warehouses offer easier accessibility and sharing of data. With a cloud-based system, data can be accessed from anywhere with an internet connection. This enables companies to collaborate with team members and external partners in real-time, leading to faster decision making and increased productivity.

Thirdly, cloud-based data warehouses offer improved security and disaster recovery. Data is stored in redundant servers in multiple locations, ensuring data is protected against loss, corruption, or unavailability. This provides peace of mind to businesses, knowing their data is secure and can be easily recovered in case of any disruptions.

Finally, cloud-based data warehouses provide better cost-effectiveness. With no need to invest in expensive hardware or software, and minimal maintenance costs, businesses can save a lot of time and money, while still having access to reliable technology.

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