Top 26 Data Warehouse Interview Questions & Answers [2022]


Data Warehouse Interview Questions & Answers

Firms and IT companies have data warehouses that hold data accumulated from several sources, which are then used in decision-making at the management level. If you are eyeing a data warehouse position in any organization, you have to prepare for your interview adequately. Therefore, this article will look at some of the most common data warehouse questions in interviews to increase your chances of landing the job.

1. What Do You Understand by a Data Warehouse?

A Data warehouse is a large store of data accumulated from different sources to guide management decision-making. Firms normally invest in warehouses for short and long-term purposes since such data contain business conditions. A data warehouse accumulates integrated information that an organization can use for queries and analyses.

2. Can You Please Define an Active Data Warehouse

An active data warehouse, also known as an ADW, accumulates several services, products, features, and business partnerships that help in real-time or near real-time making of operational decisions in the workplace. It is quite different from traditional data warehouses, mainly designed to help managers or business executives make decisions for strategic purposes. To be precise, it is a data warehouse that only supports real-time or near real-time decision making, hence the name active.

3. What Do You Understand by Data Mining?

Data mining is the process of extracting and identifying patterns or insights in large data sets. It identified the anomalies, correlations, and patterns between large data sets. Data mining makes use of machine learning, statistics and database systems. It is mostly used in system-level designing to help extract and understand the relationship between the customer’s needs, product portfolio, and architecture. Most people rely on data mining to predict the time, cost, and dependencies of the development of a product. Those are just but a few uses of data mining.

4. Do You Know Who Uses Data Mining?

Data mining is used in several industries and sectors owing to its applications and advantages. However, the most common is the financial sector which relies on it to establish different patterns in the market to stay afloat. Governments also use it to identify serious potential security threats. Moreover, other corporations such as social media companies mine their users’ data for advertising and marketing purposes. It, therefore, has varied applications, which explain why it is common or popular. Almost every sector with a large user or customer base relies on data mining.

Why Should We Hire You? 5 Best Answ...
Why Should We Hire You? 5 Best Answers

5. Can You Define OLAP?

OLAP, which is fully known as Online Analytical Process, is a system that not only collects but manages and processes multi-dimensional data. This is highly important for management and analysis purposes. It is used to answer multi-dimensional analytical queries faster when computing. One can also say that it is part of business intelligence and consists of report writing, data mining, and rational databases. Its goal is to fasten data analysis through precalculation and premeditation. It thus stores data in an OLAP database or cube.

6. What is OLTP?

Fully known as Online Transaction Processing, OLTP is a system or application that comes in handy whenever many simultaneous users receive data. It is highly accessible and is used in several enterprises to execute transaction-based or focused tasks.  It helps in executing several transactions occurring simultaneously, be it shopping, order entry, banking, or messaging. It is part of the two data processing capabilities: OLTP and OLAP. Good examples include retail sales and financial translation systems. Mostly, OLTP is integrated into SOA, or service printed architecture and web services.

7. Kindly Differentiate Between OTLP and OLAP.

There exist a number of differences between OLTP and OLAP. One of the main differences is the origin of data in these two systems. In OLTP, data comes from an original data source, whereas in OLAP, data originates from several data sources. OLTP handles fundamental business tasks, whereas OLAP focuses on multi-dimensional business tasks. OLTP also deals with simple queries by users, whereas OLAP deals with complex queries by the system. OLTP focuses on small normalized databases, while OLAP is about the large denormalized database.

8. Could You Explain What ODS is?

ODS, fully known as the operational data store, is a repository that handles real-time operational data. It is quite different from the other types that focus on long-term data trends. It is a data store used for operational reporting. It is also one of the data sources for the enterprise data warehouse. It is normally used to integrate disparate data from a number of sources with the main aim of conducting business operations, reporting, and analysis simultaneously. It offers a snapshot of the latest data from various transactional systems. Organizations can, therefore, easily combine different data formats.

9. What Do You Understand by Business Intelligence?

Business Intelligence is also known as DSS or decision support system. It encompasses all the technologies, practices, and applications involved in integrating, collecting, and analyzing business-related information or data. It refers to all the strategies and technologies used by different enterprises to analyze data relating to business information. It has a set of technologies that offer businesses different views of business operations, such as current, historical, and predictive. This data analysis approach relies on real-time business analytics to integrate real-time data into the operational system, thus aiding in timely usage.

10. What is a Dimension Table?

A dimension table in data warehousing is a table that stores various attributes or dimensions describing objects in the fact table. It can also be explained as a star schema of a data warehouse. It is important in a data warehouse as it organizes descriptive attributes in columns. Moreover, it is worth mentioning that a dimension table is also known as a dimension entity and can also be defined as an entity in a snowflake, star, or star flake schema whose main purpose is to store details about facts.

11. What is a Fact Table?

A fact table is made up of the measurement of different business processes. It is normally denormalized and holds quantitative information for analysis.  It is essential to the dimension table as it has its foreign keys. It can also be defined as a cumulation of the metrics or facts of the business processes and has an actual location in the star schema as it is found in its center while surrounded by dimensional tables. Where there are several fact tables, they are normally arranged as a constellation schema.

12. Tell Us What ETL is

ETL, fully known as Extract, Transform, and Load, is a software operation often used to read data from a given data source. It then extracts the needed subset of the data and transforms it using rules and look-up tables. After transformation, the software converts the data to the right state. The load function in ETL then loads this data into the target database. In short, ETL is the procedure involved in copying data from one or given sources into a final system that changes the data into the needed state. It is important for businesses as it helps them gather data from different sources and bring it to one centralized location.

13. What Do You Understand By Real-Time Data Warehousing?

Real-time data warehousing, just like the name suggests, captures business data as they occur. The data can be accessed the moment the business activity is completed and is normally available in the flow for immediate purposes. This makes it one of the most valuable data warehouses for business. Businesses need real-time data warehousing for everyday or short time business decisions. However, it may not come in handy for strategic planning.

14. What Do You Understand by SCD?

SCD, fully known as a slowly changing dimension, is an important part of the data warehouse that stores and manages current and historical data over a defined period. This is one of the most important ETL tasks due to its importance in tracking the history of dimension records. An example of SCD is type 1 SCD. Here, the existing data is overridden by the new one, which is common in instances where records change periodically. SCD 2 is pretty different from the first since instead of replacing the new record, it is added to the dimension table. The final SCD is SCD 3, were to include any new data, the original one has to be modified.

15. You Mentioned a Star Schema at One Point. What is it?

Star schema is one of the most common approaches when dealing with data warehouses and dimensional data marts. This is because it is the simplest data mart schema style, and therefore easier to understand. At a glance, one notices one or more fact tables that normally reference a wide number of dimension tables. Therefore, it can be described as an optimized database organizational structure that used business intelligence with a large fact table for storing transactional data.

16. Could You Please Tell Us What a Datamart Is?

A data mart is a special type of data warehouse with operational data snapshots. It analyses past trends and experiences, thus helping people and businesses make good decisions. It helps in easy access to relevant data or information. It is normally used to recover or retrieve client-facing data and focuses on a particular business line or team. There are three different data mart types: hybrid, independent, and dependent. This categorization is based on the data mart’s relation to the data warehouse and different data sources.

17. What is an ER Diagram?

An ER Diagram, fully known as an entity-relationship diagram, shows the interrelationships between different entities in a data set. It normally outlines the structure of every table and the links between them. A normal ER Diagram usually has entity types and details the relationships between different entities. There are two types of ER diagrams, namely conceptual and physical. These entity-relationship diagram models are often used as the foundation for logical data models.

18. What is Data Cleansing?

Data cleansing is quite a straightforward term. The name suggests what it is. It is a process that detects and removes any corrupt or inaccurate data from a given source, be it a record set, database, set, or table. The process identifies incomplete, inaccurate, irrelevant, or incorrect parts of a given data before deleting, replacing, and modifying the dirty data. It is quite costly, which explains why organizations invest in it. For a database with over 10,000 records, an entity can spend up to $15,000. It comes with a number of benefits, such as increased productivity and time conservation.

19. Define Metadata

Metadata can be defined as a set of data that offers more information about a given set of data. In simple terms, it is data about the data. Good examples of metadata are the author, date created, file size, and date modified. You will also find metadata in unstructured data, including images, videos, spreadsheets, and web pages. It is mostly used by search engines and can be created manually or automatically.

20. Define Surrogate Key

Surrogate keys are also known as synthetic keys, pseudo keys, entity identifiers, factless keys, or technical keys. They are found in databases and act as unique identifiers for either the database object or modeled world entity. Unlike the business key( also known as a natural key), the surrogate key does not come from application data.

21. Can You Mention the Steps of Building a Data Warehouse

Building a data warehouse is not as easier as it sounds. There are several steps involved. First, an entity must gather the business requirements, identify relevant and necessary sources, gather facts, define different dimensions, define attributes, and redefine the dimensions and attributes if necessary. It should then plan out the attribute hierarchy, define relationships and assign the specialized identifiers.

22. Define Partial Backup

A partial backup resembles a full database backup, just that it lacks all the filegroups. It comprises the data found in the primary filegroup, optionally specified read-only files, and every read/write filegroup. It can be defined as any operating system backup that doesn’t level up to a full backup, which is normally taken when the database is open or shut. Some examples of partial database backups include backup of a control file, backup of single data files, and backup of all data files for specific data spaces.

23. Mention Some of the Types of Data Warehouses

There are three main types of data warehouses: enterprise data warehouse, operational data store, and data mart. Enterprise data warehouse, shortened as EDW, is normally centralized. As the name suggests, it offers decision support services for the whole enterprise. An operational data store is also centralized and is used for operational reporting. It is one of the data sources for enterprise data warehouses. Lastly, the data mart is a subset of a data warehouse. It usually focuses on a specific team or business line. It is also subject-oriented, allowing only specified users to obtain data.

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24. Mention How Data Warehouses Work

A data warehouse is a centralized location that receives and keeps information from different sources. Data flows in any format, be it structured, unstructured or semi-structured. Data may also arrive from customer-facing applications, external systems, and internal applications. The data is then ingested, transformed, and processed. The processed data is then used in decision-making. Data warehouses, therefore, merge large quantities of information which helps in decision making. There are different types of data warehouses with varied functionalities.

25. What are Loops?

Loops exist between the tables. Whenever they exist, query generation takes longer and creates more ambiguity. IT professionals and data engineers are normally advised to avoid loops. They can also be defined as multiple paths in tables selected on queries or closed path structures. There are several ways of resolving them, such as Alias, an exact duplicate of the original table, and tricking the SQL query with a different name, forcing it to accept the usage of two tables. Whenever there is a single lookup table, one should use an alias. 

26. What Do You Understand by Aggregate Tables?

Aggregate tables refer to tables with existing warehouse data grouped to specific levels of dimensions. Such tables make it easier to retrieve data compared to the original tables. It does away with the number of records occasioned by the latter. Aggregate tables are essential since they reduce the data server load. Instead, they increase query performance which is important. These tables roll up or aggregate data and contain several functions such as min, max, count, and average. It is also worth mentioning that aggregate facts tables are types of fact tables.

Conclusion

You must have realized that most of these questions are technical. Therefore make sure that you focus more on the technical aspects of data warehousing to increase your chances of landing the job.

However, do not fail to work on your general interview skills and posture. The interviewer may also ask you a number of general and behavioral questions to define whether you are fit for the position. Therefore, do not take anything to chance.

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