One of the most frequently asked Data Warehouse interview questions is: what is a Data Warehouse? The answer is simple: a Data Warehouse (DW or DWH), also known as an enterprise Data Warehouse (EDW), is a framework used for documentation and data collection that is a crucial element of business intelligence.
DWs are primary archives of combined data from one or more outlets. They consolidate current and past events and use them to provide predictive reports for employees around the organization.
If you have an upcoming interview lined up for the position of Data Warehouse Analyst in the Business Intelligence industry, you must be able to answer some simple to complicated Data Warehouse interview questions. Here’s our concise list of the most essential Data Warehouse interview questions backed by intensive research and tailor-made for all aspiring Data Warehouse analysts.
Data mining refers to the study of large amounts of data to identify valuable perspectives such as trends, topics, and patterns. Data mining is characterized as a method for extracting valuable data from a more extensive collection of raw data. It entails using one or more tools to analyze data patterns in vast batches of data. It is one of the most common Data Warehouse interview questions.
A Data Warehouse is an extensive collection of data gathered by various retail outlets by a particular customer or enterprise to bring positive outcomes while improving their business choices. A Data Warehouse is intended to run queries and analyses on historical data obtained from transactional sources for business intelligence and data mining purposes.
It is a single dimension that is used to store the tiny dimensions known as junk attributes. The junk attributes in this phase are a set of text attributes and flags that are transmitted into a different sub-domain known as junk dimension. It is a dimension table containing properties that do not fit either the truth table or the current dimension tables. These features are usually text or multiple flags, such as non-generic comments or basic yes/no or true/false markers.
OLAP stands for Online Analytical Procedure, and it is a method of managing, processing, and collecting multidimensional data for collection and research. It is an important concept for Data Warehouse interview questions.
The term refers to the storage of data for a specific field such as product, customer, or sales. The subject-oriented property denotes that the data in a DW are grouped around major bodies in an organization’s interests. Customers, brands, prices, and vendors are examples of topics. This property enables DW users to do in-depth analyses on each topic for operational and strategic decision-making.
A data center is a vast storage facility for data obtained by various sources in an enterprise. Although the data mart functions as a branch of a data center, it gathers data from a specific department in the sector. The data mart is small (lower than 100GB) and has a plain interface in terms of size. The Data Warehouse, whereas ETL, has a dynamic architecture and occupies greater than 100GB.
Metadata is information about other data that is contained inside a collection of data. Metadata summarizes essential facts about data, making it easy to search and deal with specific instances of data. Metadata can be generated both manually and automatically.
The transactional data is captured and reposted by Active Data Warehousing. This method allows the corporation to consider habits, behaviors, and consumers to make future choices about the company’s logo.
ODS stands for the operational data store, and it stores the most recent data transfers from various operational sources and activities over data and monitoring. A central archive that provides a snapshot of the most recent data from multiple transactional processes for operational monitoring is known as an operational data store (ODS). It allows businesses to consolidate data in its original format from several sources into a single destination for market reporting.
Slowly changing dimensions are referred to as SCD. Changes in data occur gradually in these dimensions. When you want to collect shifting data within a dimension over time, you can use slowly-changing dimensions. There are three methods for gradually shifting measurements.
Metadata is classified as information about information. However, the data dictionary contains information about the mission, ab initio commands, tables, and server information.
OLTP is an abbreviation for Online Transaction Processing, which is concerned with transaction-oriented data. It uses programs to upload, edit, and uninstall a tiny quantity of data from the database. It applies to user purchases.
The term is an acronym that stands for Extract, Transform, and Load. The machine gathers data from various sources and consolidates it in the center spot. The ETL method is critical in data integration techniques. ETL enables organizations to collect data from multiple channels and integrate it into a single, unified location. ETL also allows various types of data to interact with one another.
Testing is carried out to validate data extracted from various sources and destinations. So, what are the ETL Tester’s responsibilities?
Staging Layer: It stores data gathered from various sources.
Access Layer: End-users focus on data collection in this layer by gaining the information from this layer.
Data Integration Layer: Imports data from the staging layer into the database. The information may be classified as facts, aggregates, or dimensions. Dimensions and Facts may be combined to form the schema.
Loops occur between the tables in data warehousing. If there is a loop between the tables, the question generation can take longer and result in uncertainty. It is best to prevent looping between tables.
Data Stage, Oracle, Informatica, Ab Initio, Warehouse Builder, and Data Junction are some of the resources available for extracting, transforming, and loading data.
A view is simply a graphical table that accepts the query’s output and can be used in place of tables. A materialized view is just an indirect reference to table data that is achieved by storing the results of a questionnaire in a different schema.
Real-time data warehousing records enterprise data as it happens. When a market process is done, the data is visible in the flow and ready for immediate use. The warehouse is modified every time the device trades in real-time data warehousing. It represents the company’s real-time data. This ensures that when the question is executed in the warehouse, the company’s current state is retrieved.
Using XMLA as XML for testing purposes is an effective method for gathering information from OLAP, data mining, and other online sources. XMLA is a simple object management protocol. The protocol makes use of two methods: Discover and Execute. The discovery system collects data from the archive, while the execution system helps you execute programs against data sources.
The only table that can be connected to the truth table is the primary dimension table. A Data Warehouse snowflake schema is a mathematical structural representation of tables in which the ER diagram resembles a snowflake shape. It is an extension of a Star Schema that adds depth. The dimension tables have been normalized, resulting in the data being divided into additional tables.
Data analytics analyzes raw data to make future predictions and draw conclusions based on the information provided. Data is gathered and examined to answer queries, confirm assumptions, or refute theories. However, data must be stored or structured before it can be analyzed. Data Analytics is a must for students preparing Data Warehouse interview questions.
The distinction between OLAP and OLTP Data Warehouses is a product of the process between these two processes. OLAP brings data from various channels, while OLTP processes real-time transaction data from users by adding, modifying, and extracting them in tiny amounts of data through apps.
A Data Warehouse is a location where all data is stored for analysis, while OLAP is used for data analysis, handling aggregations, and information partitioning into minor level information.
Dimensional Modeling is classified into three groups, which are as follows:
Cubes are used to describe multidimensional data logically. The dimension members are located on the cube’s edge, and the data values are located on the cube’s body.
Yes, since dimension tables are informative elements of our industry, they may have numerical values.
During the execution plan, the optimizer takes two methods. These are their names:
Dimensional Modeling is a concept that Data Warehouse designers may use to build their own Data Warehouse. Dimensional Modeling (DM) is a data structure methodology explicitly designed for data collection in a Data Warehouse. Dimensional modeling aims to customize the database for faster data retrieval. This model can be saved in two different categories of tables: Facts and Dimensions.
The fact table contains the details and measurements of the company, while the dimension table contains the background of the measurements.
The stages that are involved in Data warehouse are as follows:
Non-addictive evidence is those that cannot be summed with all the dimensions in the truth table. The same facts will be helpful if the proportions change. For specific non-additive measurements (such as price), you can correctly determine a summation by adding the numerator (dollars) and dividing by the number of the denominators (units).
Information purging refers to the process of permanently removing data from a computer.
A Datamart is a customized version of data warehousing that provides a rundown of organizational data that assists business people in making decisions based on historical patterns and experiences. A data mart will help to highlight the importance of quick access to specific information. A Data Mart is a subset of data collected in a Data Warehouse based on a specific operating region of an enterprise. A Data Mart is a streamlined variation of a Data Warehouse that is intended for use by a single department, team, or group of users within an enterprise. For example, marketing, sales, human resources, or finance.
The tables in Star Schema are arranged so that the results can be obtained quickly in the database world. The most basic Data Warehouse schema is the star schema. A star schema is so named because the diagram resembles a star, with points radiating from a center. The star’s center comprises one or two truth tables, and the star’s points are dimension tables.
SCD is classified into three types:
SCD1: The latest record replaces the previous record.
SCD2: A new record has been added to the dimension table.
SCD3: New data is introduced by altering the original data.
VLDB is an abbreviation for Very Large Database and is a one-terabyte database. The archive necessitates a vast number of database rows as well as disk capacity for the biggest file. For a significant variety of people, this database employs transaction analysis and decision support applications. A Very Large Database (VLDB) is an area or storage space maintained by a relational database management system (RDBMS) that contains massive amounts of information. Every day, the concept of a VLDB evolves as hardware and software adapt, becoming quicker and faster – and capable of managing ever-increasing loads.
Business intelligence is often referred to as DSS – Decision support system, and it relates to the technologies, applications, and practices for gathering, integrating, and analyzing business-related knowledge or data. And seeing the statistics on the information itself is beneficial. Data warehousing and business intelligence are concepts used to characterize the method of storing all of the company’s data in internal or external databases from different sources to analyze it and produce actionable information through online BI software.
Aggregate tables include current warehouse data that have been clustered to a specific degree of dimension. Data is easier to obtain from aggregated tables than from the original table with a larger number of records. Aggregate tables aggregate or “roll-up” data to a degree greater than a base or derived table. This table reduces the burden on the database server and improves query efficiency.
Entity-Relationship Diagram (ER diagram) is an abbreviation for Entity-Relationship Diagram, which depicts the interrelationships between the entities in a database. This diagram illustrates the arrangement of each table as well as the relationships between them. An Entity Relationship Diagram (ER Diagram) is a flowchart that shows how “entities” in a structure interact with one another. An object is a being that can exist independently and can be uniquely defined.
In a fact table, the BUS schema consists of a suite of verified dimensions and uniform definitions. The BUS Schema handles the Dimension Identification for all businesses. BUS Schema in ETL has a standardized description of details as well as a conformed dimension.
A Data Warehouse enables you to capture and handle data, which can then provide valuable market insights. At Jigsaw, we believe in nurturing potential and helping students achieve their long-term career goals. With these advanced Data Warehouse interview questions, you can prepare well in time to ace that interview! Looking for a course for more in-depth expertise in Data Science? Check out Jigsaw’s course on Data Science and learn more Data Warehouse interview questions here!