Data Warehousing Interview Questions and Answers

  1. What is Data Warehousing? Data warehousing is the process of storing and managing data from varied sources to provide meaningful business insights. It involves data integration, cleaning, and transformation to support decision-making processes.

  2. What is Business Intelligence (BI)? Business Intelligence (BI) refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. It helps organizations make data-driven decisions.

  3. What is a Dimension Table? A dimension table is a table in a data warehouse that stores attributes or dimensions that describe the objects in a fact table. It provides context for measures in the fact table.

  4. What is Dimensional Modeling? Dimensional modeling is a design technique for organizing data in a data warehouse. It involves creating dimension tables and fact tables to provide a structure that is optimized for querying and analysis.

  5. What is a Fact Table? A fact table is a central table in a star schema of a data warehouse. It stores quantitative data (facts) for analysis and is typically denormalized.

  6. What are the Fundamental Stages of Data Warehousing? The fundamental stages of data warehousing include data extraction, data transformation, data loading, and data presentation (or querying and reporting).

  7. What are the Different Methods of Loading Dimension tables? Dimension tables can be loaded using Full Refresh (truncate and load), Incremental Load (only new or changed data), and Upsert (insert new rows and update existing ones).

  8. What is Data Mining? Data mining is the process of discovering patterns and insights from large datasets using techniques such as statistical analysis, machine learning, and artificial intelligence.

  9. What is the Difference between a View and a Materialized View? A view is a virtual table based on a SQL query, while a materialized view is a physical copy of the view's result set stored in the database, updated periodically.

  10. What is OLTP? Online Transaction Processing (OLTP) is a class of systems that manage transaction-oriented applications, typically for day-to-day operations in businesses.

  11. What is OLAP? Online Analytical Processing (OLAP) is a class of systems that support complex analysis of data for decision-making, usually in data warehousing environments.

  12. What is the Difference between OLTP and OLAP? OLTP is focused on transaction processing with high throughput and low latency, while OLAP is focused on analytical processing with complex queries and aggregations.

  13. What is ODS? Operational Data Store (ODS) is a database designed to integrate data from multiple sources for operational reporting and decision support.

  14. What is ER Diagram? An Entity-Relationship (ER) diagram is a visual representation of data entities and their relationships in a database.

  15. What is ETL? ETL (Extract, Transform, Load) is the process of extracting data from source systems, transforming it into a format suitable for analysis, and loading it into a data warehouse.

  16. Is OLTP Database Design Optimal for Data Warehouse? No, OLTP databases are optimized for transactional processing with normalized schemas, whereas data warehouses typically use denormalized schemas optimized for analytical querying.

  17. If denormalizing improves Data Warehouse Processes, then why is the Fact Table is in the Normal Form? The fact table is typically in a normalized form to maintain data integrity and reduce redundancy. Denormalization is applied selectively to dimension tables to improve query performance.

  18. What are Lookup Tables? Lookup tables are small tables in a data warehouse that store commonly used values, such as codes or categories, to simplify data maintenance and improve query performance.

  19. What are Aggregate Tables? Aggregate tables store precomputed summaries of data to improve query performance for commonly used aggregations and reports in a data warehouse.

  20. What is Real-Time Data-Warehousing? Real-time data warehousing involves capturing and processing data with minimal latency, enabling immediate analysis and decision-making based on the most current data.

  21. What are Conformed Dimensions? Conformed dimensions are dimensions that have the same meaning and structure across different data marts or data warehouse environments, ensuring consistency in analysis.

  22. What is a Conformed Fact? A conformed fact is a fact table that is shared and consistent across different data marts or data warehouse environments, enabling consistent metrics and analysis.

  23. How do you Load the Time Dimension? The time dimension is loaded with dates and other time-related attributes (year, month, day, etc.) using scripts or ETL processes. It can be populated using predefined calendars or generated dynamically.

  24. What is a Level of Granularity of a Fact Table? The level of granularity refers to the level of detail or aggregation in a fact table. It defines the specific combination of dimensions and measures represented by each row in the fact table.

  25. What are Non-Additive Facts? Non-additive facts are facts that cannot be summed up across all dimensions. Examples include ratios, percentages, and averages.

  26. What is a Factless Facts Table? A factless fact table is a fact table that contains no measures but captures events or relationships between dimensions, serving as a bridge for many-to-many relationships.

  27. What are Slowly Changing Dimensions (SCD)? Slowly Changing Dimensions (SCD) are dimensions that change slowly over time, requiring historical tracking. SCD types include Type 1 (overwrite), Type 2 (add new row), and Type 3 (add new attribute).

  28. What is Hybrid Slowly Changing Dimension? A hybrid SCD combines different SCD techniques (e.g., Type 1 and Type 2) to handle different attributes within the same dimension table based on their change behavior.

  29. What is BUS Schema? BUS schema is a methodology for organizing data warehouse schemas around core business processes or events, integrating multiple data marts and facilitating cross-functional analysis.

  30. What is a Star Schema? A star schema is a data warehouse schema design consisting of a centralized fact table connected to multiple dimension tables, resembling a star when viewed graphically.

  31. What Snow Flake Schema? A snowflake schema is a data warehouse schema design where dimension tables are normalized into multiple related tables, resembling a snowflake when viewed graphically.

  32. Differences between the Star and Snowflake Schema? In a star schema, dimensions are denormalized into a single table per dimension, while in a snowflake schema, dimensions are normalized into multiple related tables.

  33. What is Difference between ER Modeling and Dimensional Modeling? ER (Entity-Relationship) modeling is used for transactional databases, focusing on entity relationships and normalization, while dimensional modeling is optimized for analytical databases, focusing on facts and dimensions.

  34. What is Degenerate Dimension Table? A degenerate dimension table is a dimension table that consists of attributes that are part of the fact table itself, typically as part of the fact table's composite primary key.

  35. Why is Data Modeling Important? Data modeling is important because it provides a blueprint for designing databases or data warehouses that accurately represent business requirements, ensuring data integrity, and optimizing query performance.

  36. What is a Surrogate Key? A surrogate key is a unique identifier assigned to each record in a dimension table to ensure a consistent, non-changing primary key for referencing in fact tables and maintaining historical data integrity.

  37. What is Junk Dimension? A junk dimension is a small dimension table that combines several low-cardinality attributes and flags into a single dimension to simplify the data model and reduce the number of joins in queries.

  38. What is a Data Mart? A data mart is a subset of a data warehouse focused on a specific business line, department, or functional area, containing summarized and relevant data for specific types of analysis.

  39. What is the Difference between OLAP and Data Warehouse? A data warehouse is a repository of integrated data from multiple sources, while OLAP refers to the analysis techniques and tools used to analyze data stored in a data warehouse.

  40. What is a Cube and Linked Cube with Reference to Data Warehouse? A cube is a multidimensional data structure that stores data aggregates for fast querying and analysis. A linked cube combines data from multiple cubes to support integrated analysis across different perspectives.

  41. What is Snapshot with Reference to Data Warehouse? A snapshot in a data warehouse refers to a point-in-time copy of data to capture historical changes or versions of data at specific intervals, facilitating trend analysis and reporting.

  42. What is the Difference between Data Warehousing and Business Intelligence? Data warehousing involves storing and managing data from diverse sources, while business intelligence involves analyzing and presenting that data to support decision-making and strategic planning.

  43. What is MDS? MDS (Master Data Services) is a Microsoft SQL Server feature used for managing and maintaining master data within an organization, ensuring consistency and accuracy across different systems.

  44. Explain the Paradigm of Bill Inmon and Ralph Kimball. Bill Inmon and Ralph Kimball are two prominent figures in data warehousing. Inmon advocates for the top-down approach with a centralized data warehouse, while Kimball promotes the bottom-up approach with dimensional modeling and data marts.

  45. Normalization vs. Denormalization: When would you choose to denormalize data in a data warehouse? What are the advantages and disadvantages of denormalization? Denormalization is chosen in data warehousing to improve query performance by reducing the need for joins and aggregations

Comments

  1. Interview questions answers pdfJune 10, 2016 at 4:42 AM

    Data Warehousing Interview Questions and Answers
    http://allinterviewquestionsandanswerspdf.blogspot.in/2016/06/top-100-data-warehousing-interview.html

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  2. Sivanandhana GirishMarch 14, 2019 at 3:07 AM

    Thanks for your efforts in sharing this information in detail. This was very helpful to me. kindly keep continuing the great work.

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