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Data Management Question and Answer for SQL Interview


Data Management (MDM) is critical to a well-functioning data warehouse, and SQL emerges as a valuable ally in making this happen. SQL, a powerful and versatile language, simplifies the creation of master data models, defining attributes, establishing relationships, and applying validation rules. It seamlessly integrates with DM, allowing data warehouse teams to develop holistic solutions, covering attributes and hierarchies not handled in operational systems. 

For those preparing for an SQL interview, these must-know Data management interview questions and answers will help you ace it.

Q1: How Do Master Data And Dimensions Relate, And Why Is A Master Data Management System Valuable For The Dimension Table ETL Process In A Data Warehouse?

A: Master data and dimensions are closely linked but not identical. Master data, ideally situated near transaction systems, is vital for data warehouses. Implementing a robust master data management system within the transaction environment greatly benefits data warehouse architects. 

This setup simplifies complex challenges in the Extract, Transform, Load (ETL) process for dimension tables. Essentially, a well-established master data management system becomes a crucial resource for providing dimension data in a data warehouse, streamlining the overall data management process.

Q2: Why Do Transaction Systems Often Lack The Necessary Descriptive Attributes For Comprehensive Reporting?

A: Transaction systems designed for processing transactions often need more comprehensive descriptive attributes for robust reporting and analytics. Many organizations rely on purchased ERP systems for critical operations, which can be challenging to extend. When gathering business requirements for a data warehouse, common complaints from users involve incomplete attributes, focusing on missing alternate rollups or hierarchies. 

Business users frequently maintain additional product rollups or organizational hierarchies in unmanaged applications like Excel or Access, contributing to the challenge of incomplete attributes in transaction systems.

Q3: Why Is Integrating Information From Multiple Source Systems A Vital Goal Of A Data Warehouse, And What Challenges Arise When Dealing With Entities Like Customers Sourced From Various Transaction Systems?

A: A crucial objective of a data warehouse is integrating information from diverse source systems. This becomes incredibly challenging when dealing with entities like customers, often sourced from multiple transaction systems, a situation commonly observed in growing organizations, mainly through acquisitions. 

Even within a unified company, sales and customer care systems may maintain separate copies of customer attributes. The business objective is establishing a unified master source for customer data, consolidating information from all transaction systems and subsidiaries for a comprehensive and centralized view.

Q4: Why Is Maintaining A Master Data Management System That Performs Data Integration Challenging?

A: Maintaining a master data management system focused on data integration poses ongoing challenges. The system and business users continually react to data input from various source systems, each with a diverse range of data quality. 

While building such a system, there's an opportunity to enhance data governance. However, it's essential to recognize that data integration is a temporary solution—often a necessary step toward a more comprehensive and enduring solution despite its inherent complexities and ongoing maintenance struggles.

Q5: What Features Does Microsoft's Master Data Services (MDS) Offer, And How Does It Contribute To Managing Master Data?

A: Microsoft's Master Data Services (MDS) brings several essential features to master data management. These include a user-friendly interface for defining master data structures (models), a robust database structure for storing master data, and comprehensive security covering both model definition and data management. 

MDS also offers hierarchy management with secure user restrictions, programmability, full versioning of models and attributes, a user interface for data management with workflow support, and mechanisms for importing, updating, and exporting data. These features collectively enhance the efficiency and control of managing master data compared to traditional methods without dedicated master data management software.

Q6: How Does A Master Data Management Solution Facilitate User Participation In Data Management, Especially By Business Users, And What Security Measures Are In Place To Control Access To Tasks?

A: A key advantage of a master data management solution lies in enabling user participation, including business users, in data management tasks. This proves crucial for adding attributes and managing hierarchies not sourced from existing transaction systems. 

To ensure secure access, the solution allows assigning permissions for tasks such as modifying model structures, adding or modifying data within a model, and specifying users authorized to modify specific hierarchy branches. These security measures are administered through the Master Data Manager web application, ensuring controlled and protected access to various aspects of data management.

Q7: Can You Explain The Function And Structure Of The Master Data Management System's Three Staging Tables (TblStgMember, TblStgMemberAttribute, TblSTGRelationship) Used In The Master Data Management System?

A: The three staging tables—tblStgMember, tblStgMemberAttribute, and tblSTGRelationship—serve essential roles in the master data management system.

tblStgMember: This table stages new members (e.g., products or customers) by providing member names, identifying codes, and source system keys. It includes metadata describing the target model, entity, and data owner.

tblStgMemberAttribute: Used for staging attribute values for each member, it accommodates a canonical form with one row per attribute. The column for attribute values supports various data types, and metadata, including member code, facilitates attribute mapping. Existing attributes are updated, and changes are logged using the MDMMemberStatus system attribute.
tblSTGRelationship: This table is instrumental in adding or maintaining parent-child or explicit hierarchies, contributing to the overall effectiveness of the master data management system.

Q8: What Are The Notable Features Of The Export Views,Particularly In Terms Of Metadata?

A: The export views offer compelling features with enriching metadata. Noteworthy aspects include:

  • Version: Enables filtering for extracting only the current model's data.

  • Member entered and last updated date and time, user, and model version: Facilitates extraction of rows added or changed since the last data warehouse update, providing valuable insights into data evolution.

Validation status: Allows filtering to extract rows adhering to all the model's business rules, ensuring that the exported data meets predefined quality and compliance criteria. These features collectively enhance the precision and efficiency of data extraction from the master data management system.

Q9: Can You Elaborate On The Role Of Master Data Services (MDS) In Simplifying The ETL System, Particularly In Managing The History Of Dimension Attributes?

A: As part of the dimensional design process for the data warehouse, decisions about managing dimension attribute history as type 1 (restate history) or type 2 (track history) are crucial and typically based on business user requirements. While these decisions are made once and apply universally, there's often a need to adapt them as the design evolves. 

Master Data Services (MDS) proves valuable in this scenario, simplifying the ETL system. It facilitates the management of attribute history, including type 1 attributes, by storing historical data in the data warehouse staging area and relying on MDS for history tracking. It streamlines the ETL process, ensuring a more straightforward and efficient system for managing evolving attribute requirements.

Q10: How Does The Master Data Manager Facilitate Bringing Candidate Rows From Staging Tables Into Master Data Services (MDS)?

A: The Master Data Manager streamlines the transition of candidate rows from staging tables to MDS. In the Data Integration section, selecting Import initiates this process. Each staged row undergoes updating with batch information, current status, and error codes; all meticulously logged for tracking purposes. MDS intelligently captures only unprocessed rows, allowing continuous data appending to the staging tables. However, it's advisable to establish a periodic process for pruning fully staged rows.

For efficiency in production scenarios, an automated import process is recommended over manual initiation through the Master Data Manager. Utilizing processing stored procedures, notably updatedStagingSweep, in the MDS database proves to be the most straightforward and efficient way to achieve this.

Q11: Could You Provide An Overview Of The Initial Tasks Defining A Master Data Services (MDS) Model?

A: Defining a Master Data Services (MDS) model involves several critical initial tasks:

  • Create the Model: Establish the foundational structure for managing master data. Each model corresponds to a dimension in the data warehouse.

  • Create Attributes with Domains (Entities): Define attributes whose values are limited to specific sets, known as domains. An example is creating attributes like product type.

  • Create Attributes without Domains: Establish attributes that do not have predefined domains, such as product descriptions.

  • Create Hierarchical Relationships: Set up relationships between entities to form hierarchies, contributing to a more organized and meaningful data structure.

Define Business Rules for Data Validation: Establish rules to validate data, such as ensuring a numeric field like percent ownership is within a specified range. Business rules can trigger email notifications for rule violations, enhancing data quality and compliance.

Q12: How Does The Master Data Management System Bridge The Gap Between The Data Warehouse And Operational Systems, And What Role Does The Data Warehouse Team Typically Play In Developing An Entry-Level Master Data Management System?

A: The master data management system operates at the intersection of the data warehouse and operational systems. The data warehouse team can initiate the development of an entry-level master data management system, particularly for managing attributes and hierarchies not collected in operational systems.

The solution aligns closely with operational systems rather than the data warehouse in more comprehensive systems integration. While the data warehouse subscribes to integrated information, the solution's architecture typically belongs to the transactional side of the IT organization.

The data warehouse team often plays a crucial role in establishing the groundwork for robust master data management and data governance. Their early efforts demonstrate the value of integrated information to the organization, setting the stage for enhanced data management practices.

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