If you are interviewing for a data warehouse position, you will likely be asked about your experience with data warehouses and the tools that are used in them. You should be prepared to answer questions about your experience with ETL (Extract, Transform, Load), data cleansing, data mining, and reporting. In this blog post, we will discuss some of the most common interview questions for data warehouse positions. We will also provide tips for answering these questions. Good luck!
1. What is Data Warehousing?
Ans. A data warehouse is a repository of historical data that is used for reporting and analysis. The data in a data warehouse is typically extracted from multiple source systems and then cleansed and transformed into a format that is suitable for reporting and analysis.
2. What are the benefits of using a Data Warehouse?
Ans. The benefits of using a data warehouse include improved decision-making, increased efficiency, and reduced costs. A data warehouse can help organizations make better decisions by providing them with accurate and timely information. A data warehouse can also help organizations become more efficient by allowing them to consolidate their data from multiple sources into one place. A data warehouse can also reduce costs by allowing organizations to eliminate or reduce the need for custom reports.
3. How would you define ETL?
Ans. ETL is the process of extracting data from source systems, transforming it, and loading it into a data warehouse.
4. What is data Mining?
Ans. Data mining is the process of extracting valuable information from large data sets. Data miners use a variety of techniques, including statistical analysis, machine learning, and artificial intelligence to identify patterns and trends in data.
5. Define data cleansing.
Ans. Data cleansing is the process of identifying and correcting inaccurate or incomplete data. This can involve removing duplicate records, standardizing values, and repairing corrupted data. Data cleansing is an important step in preparing data for use in a data warehouse.
6. What is a Star Schema?
Ans. A star schema is a type of data warehouse schema that uses a central fact table surrounded by dimension tables. The fact table contains the measures that are used for reporting and analysis, while the dimension tables contain the metadata that is used to identify and describe the data in the fact table.
7. What is Bus Schema?
Ans. A bus schema is a type of data warehouse schema that uses multiple fact tables and dimension tables. A bus schema can be used to model complex business processes, such as order fulfillment or customer service, which involve more than one transaction type.
8. What are some common tools used in Data Warehousing?
Ans. Some common tools used in data warehousing include ETL tools, data cleansing tools, data mining tools, and reporting tools.
9. Differentiate data warehouse from data mart.
Ans. A data warehouse is a repository of historical data that is used for reporting and analysis. A data mart is a subset of the data in a data warehouse that is used for specific purposes, such as marketing or sales.
10. How do you load Data into Data Warehouse?
Ans. Data can be loaded into a data warehouse using ETL tools, direct extracts from source systems, or bulk loads.
11. What are some common challenges with Data Warehousing?
Ans. The most common challenges with data warehousing include lack of resources, lack of time, and difficulty integrating with source systems.
12.What are some best practices for designing a Data Warehouse?
Some best practices for designing a data warehouse include separating the database into tables and indexes, using star schemas to optimize performance, and using dimension tables to describe the data in the fact table.
13. What is a Fact Table?
Ans. A fact table contains measures that are used for reporting and analysis. The measures can be numeric values (e.g., sales revenue) or discrete values (e.g., gender). A fact table also has foreign keys that relate it to one or more dimension tables, which contain metadata about the data in the fact table. For example, a product sales revenue measure might have multiple dimensions such as customer ID, store ID, date of purchase, etc.
14. How do you load Time-Variant Data into a Data Warehouse?
Time-Variant data is changing with time and hence we need to update our existing records when there is any change in any record.
15. What is the difference between OLTP and OLAP?
Ans. OLTP deals with transactions that are inserted, updated, or deleted on a daily basis. In contrast, OLAP is optimized for reading data as it stores aggregated historical data which can be used to get insights into business trends over time. This makes it possible to perform analytical queries much faster than they would take on an OLTP system. As a result of this optimization for reading performance, writes to the database tend to be slower than in an OLTP system because writes require more complex processing logic and schema changes (e.g., updating aggregate tables).
16. What is Virtual Data Warehousing?
Ans. A virtual data warehouse is a logical, not physical, representation of data. The physical locations and structures of the underlying databases are hidden from end-users who interact with the virtual data warehouse as if it were a single database.
17. What is operational Data Store?
Ans. An operational data store (ODS) is an integration layer between source systems and enterprise applications such as ERP or CRM. An ODS can be used to integrate master reference information across multiple source systems or to store transaction records for later processing by ETL tools that load information into the data warehouse. Unlike a traditional database in which tables must have their own primary keys, an ODS has no primary key requirements because its purpose is only to temporarily hold transactional records before they are loaded into the data warehouse.
18. How do you populate Dimentional tables?
Ans. Dimensional tables (also known as dimension tables) are used to describe the data in a fact table. They typically contain descriptive information about a dimension, such as a name, description, and list of valid values. Dimensional tables can be populated using manual methods or automated ETL processes.
19. What is VLDB?
Ans. A very large database (VLDB) is a database that is too large to fit on one computer. To address this, VLDBs are typically divided into multiple tables that are distributed across multiple servers. This architecture allows the data to be accessed more quickly because it can be spread across multiple CPUs and disks. However, managing and querying a VLDB can be more complex than working with a single-server database.
20. What is Inmon?
Ans. The Inmon approach to data warehousing involves designing the warehouse as a series of standard star schemas. Fact tables are placed at the center of the schema, with dimension tables surrounding them. The advantage of this design is that it enables users to easily navigate from one fact table to its associated dimensions. The Inmon approach also emphasizes the importance of designing a well-integrated data warehouse that is free from data duplication.
21. What is Kimball?
Ans. The Kimball methodology for data warehousing involves designing the warehouse as a series of specialized snowflake schemas. Fact tables are placed in the center of the schema, with dimension tables surrounding them. This design allows users to easily navigate from one fact table to its associated dimensions and provides more flexibility than the Inmon approach when it comes to adding new dimensions or facts to the warehouse. However, it can be more difficult to manage and query a Kimball-designed warehouse than an Inmon-designed warehouse.
22. How do you denormalize your data?
Ans. Denormalization is the process of reducing or removing redundancy in a database. In data warehousing, denormalization typically refers to the process of storing more than one fact about an event in a single table (e.g., combining Order and Order Details tables). This makes it easier for users to retrieve information from the warehouse by eliminating joins between multiple tables. However, denormalization can lead to duplicate records that must be removed before loading into the warehouse.
23. What is SCD?
Ans. Slowly changing dimensions (SCDs) are used in data warehousing to track changes that occur over time, such as the name of a customer. There are three types of SCDs: Type I, which overwrites existing records with new information; Type II, which keeps all historical information about an entity in separate tables; and Type III, which only stores current values for attributes while keeping history elsewhere.
24. What are loops in data warehousing?
Ans. Loops are a potential problem that can occur in data warehousing when the same fact is stored in multiple tables. When this happens, the ETL process must be run multiple times to load all of the data into the warehouse. This can lead to inconsistency in the data and increased processing time. To avoid loops, it is important to ensure that each table only contains information about a single fact.
25. State the difference between Metadata and data dictionary.
Ans. Metadata is a description of data, while a data dictionary contains information about the structure and format of each column in the database. Metadata can be stored as tables on disk or loaded into memory (e.g., RAM) for faster access during runtime operations such as queries and updates. The metadata layer is responsible for mapping between physical storage locations where data resides (e.g., hard drives) to logical representations that are easier to understand by humans like us!
26. What are indexes?
Ans. Indexes help users retrieve records from large databases quickly by creating an additional table that stores pointers back to each row in its primary key columns(s). For example, suppose we had an Employee table with 100 million rows but wanted only to find employees who had the value ‘John’ in the first name column. We could use an index on the first name column to help us find John quickly without having to look through all 100 million rows.
28. What are snowflake schemas?
Ans. Snowflake schemas are a type of data warehouse design that help to reduce data duplication. They are created by dividing the fact table into multiple tables, with each table containing only a subset of the columns from the original fact table. Dimension tables are then created by combining a single column from each of the subsidiary fact tables. This design allows users to easily navigate from one fact table to its associated dimensions and provides more flexibility than the Inmon approach when it comes to adding new dimensions or facts to the warehouse.
29. What do you mean by Data Purging?
Ans. Data purging is the process of removing data from a database or data warehouse. This can be done for a number of reasons, such as freeing up storage space on a disk or reducing the size of the database to make it easier to manage. It is important to ensure that data is not accidentally deleted in the purge process, so a careful plan must be put in place before beginning this operation.
30. List some of the popular etl tools.
Ans. Some of the popular ETL tools include Informatica, IBM DataStage, Microsoft SSIS, and Oracle Warehouse Builder. These tools allow users to extract data from source systems, transform it into a format that is suitable for loading into the warehouse, and then load it into the database. They also provide features for testing and debugging the ETL process, as well as monitoring its performance.
