What is the purpose of data normalization?
Theme: Data Modeling Role: Data Engineer Function: Technology
Interview Question for Data Engineer: See sample answers, motivations & red flags for this common interview question. About Data Engineer: Designs and maintains data pipelines and databases. This role falls within the Technology function of a firm. See other interview questions & further information for this role here
Sample Answer
Example response for question delving into Data Modeling with the key points that need to be covered in an effective response. Customize this to your own experience with concrete examples and evidence
- Definition: Data normalization is the process of organizing data in a database to eliminate redundancy and improve data integrity
- Eliminating Redundancy: Normalization helps eliminate data redundancy by breaking down data into smaller, logical tables
- Data Integrity: Normalization improves data integrity by reducing the chances of data inconsistencies and anomalies
- Efficient Storage: Normalized data requires less storage space as redundant data is eliminated
- Improved Performance: Normalized data allows for faster data retrieval and query execution, leading to improved system performance
- Flexibility & Scalability: Normalized data provides a flexible and scalable database structure, making it easier to modify and expand the database
- Simplifying Updates: Normalization simplifies the process of updating data by reducing the need to update multiple instances of the same data
- Data Consistency: Normalization ensures data consistency by enforcing rules and constraints on the database
- Reduced Anomalies: Normalization reduces anomalies such as insertion, deletion, and update anomalies, which can occur when data is not properly organized
- Normalization Levels: Normalization is achieved through different levels, including First Normal Form (1NF), Second Normal Form (2NF), and Third Normal Form (3NF)
Underlying Motivations
What the Interviewer is trying to find out about you and your experiences through this question
- Technical Knowledge: Assessing the candidate's understanding of data normalization and its purpose in data engineering
- Problem-solving Skills: Evaluating the candidate's ability to identify and address data redundancy and inconsistency issues through normalization
- Data Management: Determining the candidate's familiarity with best practices for organizing and structuring data to improve efficiency and accuracy
Potential Minefields
How to avoid some common minefields when answering this question in order to not raise any red flags
- Lack of understanding: Not being able to explain the concept of data normalization accurately or providing incorrect information
- Vague or generic response: Providing a general answer without mentioning specific benefits or purposes of data normalization
- Limited knowledge: Not being able to discuss the potential benefits of data normalization in terms of data integrity, efficiency, and reducing redundancy
- Inability to provide examples: Failing to provide real-world examples or scenarios where data normalization is beneficial
- Overemphasis on normalization: Focusing solely on data normalization without acknowledging its limitations or considering other data management techniques