Relational Data Warehouse Interview Questions and Answers

Introduction

A Relational Data Warehouse is a specialized database for advanced querying and analysis. It's vital in SQL for streamlined data management, enabling efficient storage and retrieval. With a structured schema, it supports complex queries, facilitating optimal decision-making and robust business intelligence. This makes Relational Data Warehouses indispensable for enterprises seeking comprehensive insights and strategic data utilization. Today, we'll be telling you about the top must-know questions and answers that'll help you ace your SQL interview.

Q1: What Steps Should Be Taken Before Diving Into the Actual Design of a Relational Database?

Ans. To kick off the relational database design process smoothly, ensure you've wrapped up the logical model. Ensure that your object names are crystal clear and approved by the data governance team, sticking to sensible naming conventions for tables and columns. 

Confirm that all columns have the correct data types, initially derived from your modeling efforts, though adjustments may be needed post-data profiling. Lastly, during the dimensional modeling phase, it's crucial to have already pinpointed primary keys and foreign key relationships.

Q2: What Is the Recommended Approach for Defining Primary Keys in Dimension Tables, Specifically Concerning Surrogate Keys in SQL Server?

Ans. For dimension tables, employing a surrogate key managed by the DW/BI system is advised. The commonly used method in SQL Server involves enabling the IDENTITY property on the surrogate key column, ensuring it's of integer data type. Regular integers suffice if utilizing Enterprise Edition with page compression unless a larger size is necessary.

Careful consideration is needed for the Standard Edition. Options include tinyint (0 to 255, 1 byte), smallint (–32,768 to 32,767, 2 bytes), Int (–2,147,483,648 to 2,147,483,647, 4 bytes), and Bigint (–9,223,372,036,854,775,808 to 9,223,372,036,854,775,807, 8 bytes). Choose based on the expected dimension size.

Q3: How Can Metadata Elements From the Logical Model of a Dw/Bi System Be Effectively Incorporated Into the Physical Database Using Extended Properties for Tables and Columns?

Ans. When transitioning from the logical model to the physical database, leveraging extended properties for tables and views is recommended. For tables, at a minimum, include a business description using a table extended property named 'Table Description.' For dimension tables, create a 'Description' property for each column, capturing its business description, and a 'Source System' property for business-oriented source system summaries. 

Additionally, for non-key columns in dimension tables, establish an 'SCD Type' property with values indicating whether it should be '1-Overwrite History' or '2-Track History.' This approach ensures a comprehensive integration of metadata elements into the physical database.

Q4: What Role Do Files and Filegroups Play in Optimizing the Performance of Tables and Indexes in SQL Server, and Why Is It Crucial to Address Them During Table Creation?

Ans. Files and filegroups in SQL Server are critical components in determining where data is stored, significantly impacting performance. Files represent the physical operating system files housing data and transaction logs, while filegroups are collections of files managed collectively by SQL Server. 

Though additional layers in the file subsystem dictate actual data storage outside SQL Server, dealing with files and filegroups is essential during table creation. The CREATE TABLE statement necessitates a filegroup assignment, emphasizing the need to manage these elements for effective performance optimization strategically.

Q5: What Guidelines Should Be Followed When Determining the Location and Number of Files per Filegroup for Optimizing File Storage in SQL Server?

Ans. To optimize file storage in SQL Server, consider the following guidelines:

• Create an additional filegroup, designating it as the default.
• Distribute files across all available local disks and associate them with the filegroup.
• Pre-allocate a substantial amount of space evenly across all files, accommodating current and future data loads, including at least an extra year of capacity. Ideally, plan for three years ahead.

Segregate the transaction log file onto a separate disk from the data files. Adhering to these guidelines ensures efficient utilization of disk drives based on the disk subsystem configuration and the nature of the data and indexes in use.

Q6: How Do Statistics and Indexes Work Together in SQL Server Optimization, and When Is It Advisable to Customize Statistics Settings?

Ans. In SQL Server optimization, the optimizer relies on indexes and statistics detailing the cardinality and distribution of column values across query tables. Default settings for statistics often suffice, automatically creating and updating them as needed. 

However, manually updating statistics for the date column after each load is essential in extensive data warehouse databases, especially those involving data partitions or clustered fact tables. This ensures the optimizer recognizes the data availability. Additionally, consider crafting multicolumn statistics for frequently used combinations of fact table foreign keys, scripting and updating them regularly, as they won't be covered in the automatic update process.

Q7: How Can the Performance of Bi Reports and Analyses Be Significantly Enhanced in a Dimensional Dw/Bi System, and Why Are Aggregate Tables Considered a Pivotal Strategy?

Ans. Leveraging aggregate tables during the ETL process is crucial to speeding up BI reports and analyses, particularly those involving extensive data. For instance, a time series report displaying sales by month for the past two years while generating only 24 rows might need to sum up millions or billions of atomic-level rows if built directly from detailed fact tables. 

Creating aggregate tables summarizing data at a higher level than detailed fact tables proves to be the most effective approach for enhancing query performance. This involves omitting particular foreign keys or aggregating to a parent level in dimensions like product category, geographic region, or date month while keeping other dimensions at their leaf levels.

Q8: Why Should All Business Users’ Access to a Relational Data Warehouse Database Be Channeled Through Views?

Ans. Advocating for views as the access point to a relational data warehouse database for business users establishes a protective layer between users and the underlying database. This layer proves invaluable during post-production modifications of the DW/BI system. While this approach provides substantial benefits for system maintenance, it can be challenging for developers. 

Some BI studio design tools depend on foreign key constraints to identify join paths between tables, and these relationships are not automatically conveyed through views. Consequently, developers may need to depict these connections manually in the design tools.

Q9: What Is the Significance of Partitioned Tables in a Relational Data Warehouse Database, and How Do They Contribute to Scalability and Manageability?

Ans. Partitioned tables are vital in enhancing the scalability and manageability of relational data warehouse databases. A partitioned table is a large table divided into smaller, independent partitions. 

Each partition can be accessed, indexed, and managed autonomously, yet collectively, they appear and function as a single table for any query. The primary advantage lies in the streamlined manageability of extensive datasets. For huge partitioned tables, tasks such as data loading, indexing, and data backup become considerably more efficient than dealing with a single, monolithic table.

Q10: In Partitioned Tables, What Considerations Should Be Taken When Mapping Partitions to Filegroups?

Ans. When dealing with partitioned tables, mapping partitions to one or more file groups is crucial. Opting for a single filegroup simplifies management and aids in configuring storage for faster sequential access. However, it comes with the drawback that backup and restore operations occur at the filegroup level, necessitating the backup and restore of the entire table. 

Alternatively, creating partitions on separate filegroups enables setting older, static filegroups to Read-Only. This facilitates a backup process that recognizes unchanged filegroups, accelerates differential backups, and reduces their size. Moreover, it allows for a partial restore of a single partition when required, offering flexibility and efficiency in data management.

Conclusion

A Relational Data Warehouse is an intelligent organizer for complex data tasks and business insights. It arranges information neatly, making storage and retrieval a breeze. Here's where JanBask Training's SQL courses come – they're your key to mastering these concepts. 

With JanBask, you'll learn about SQL and gain skills crucial for handling data smartly and making informed decisions. Supercharge your SQL expertise with JanBask Training for a decisive edge in today's data-driven industry.