It’s a given that if you want to ace your next job interview, you first need to make sure your qualifications are worthy. But there is more you can do to help weigh the odds in your favor. Knowing your stuff is essential, yes, but so is being prepared.
In this context, we are talking about being ready for the questions that you will most likely face in the interview. All the knowledge in the world will be useless if you don’t know where to apply it. If you know what kinds of questions you will be asked, you can review the material and be ready with the best answers.
So today, we will spotlight the most commonly asked data modeling interview questions. We will start with basic questions, then work our way up through intermediate, followed by advanced ones.
But before we look at the questions, let’s take a moment and ask, “what is a data model?”
A data model organizes different data elements and standardizes how they relate to one another and real-world entity properties. So logically then, data modeling is the process of creating those data models.
Data models are composed of entities, and entities are the objects and concepts whose data we want to track. They, in turn, become tables found in a database. Customers, products, manufacturers, and sellers are potential entities.
Each entity has attributes—details that the users want to track. For instance, a customer’s name is an attribute.
With that out of the way, let’s check out those data modeling interview questions!
The three types of data models:
A table consists of data stored in rows and columns. Columns, also known as fields, show data in vertical alignment. Rows also called a record or tuple, represent data’s horizontal alignment.
Database normalization is the process of designing the database in such a way that it reduces data redundancy without sacrificing integrity.
The purposes of normalization are:
Denormalization is a technique where redundant data is added to an already normalized database. The procedure enhances read performance by sacrificing write performance.
ERD stands for Entity Relationship Diagram and is a logical entity representation, defining the relationships between the entities. Entities reside in boxes, and arrows symbolize relationships.
A surrogate key, also known as a primary key, enforces numerical attributes. This surrogate key replaces natural keys. Instead of having primary or composite primary keys, data modelers create the surrogate key, which is a valuable tool for identifying records, building SQL queries, and enhancing performance.
The main relationship types are:
This is a data model that consists of all the entries required by an enterprise.
These are the errors most likely encountered during data modeling.
The two design schema is called Star schema and Snowflake schema. The Star schema has a fact table centered with multiple dimension tables surrounding it. A Snowflake schema is similar, except that the level of normalization is higher, which results in the schema looking like a snowflake.
These are dimensions used to manage both historical data and current data in data-warehousing. There are four different types of slowly changing dimensions: SCD Type 0 through SCD Type 3.
A data mart is the most straightforward set of data warehousing and is used to focus on one functional area of any given business. Data marts are a subset of data warehouses oriented to a specific line of business or functional area of an organization (e.g., marketing, finance, sales). Data enters data marts by an assortment of transactional systems, other data warehouses, or even external sources.
Granularity represents the level of information stored in a table. Granularity is defined as high or low. High granularity data contains transaction-level data. Low granularity has low-level information only, such as that found in fact tables.
Data sparsity defines how much data we have for a model’s specified dimension or entity. If there is insufficient information stored in the dimensions, then more space is needed to store these aggregations, resulting in an oversized, cumbersome database.
Entities can be broken down into several sub-entities or grouped by specific features. Each sub-entity has relevant attributes and is called a subtype entity. Attributes common to every entity are placed in a higher or super level entity, which is why they are called supertype entities.
Metadata is defined as “data about data.” In the context of data modeling, it’s the data that covers what types of data are in the system, what it’s used for, and who uses it.
No, it’s not an absolute requirement. However, denormalized databases are easily accessible, easier to maintain, and less redundant.
Forward engineering is a process where Data Definition Language (DDL) scripts are generated from the data model itself. DDL scripts can be used to create databases. Reverse Engineering creates data models from a database or scripts. Some data modeling tools have options that connect with the database, allowing the user to engineer a database into a data model.
Recursive relationships happen when a relationship exists between an entity and itself. For instance, a doctor could be in a health center’s database as a care provider, but if the doctor is sick and goes in as a patient, this results in a recursive relationship. You would need to add a foreign key to the health center’s number in each patient’s record.
If a dimension is confirmed, it’s attached to at least two fact tables.
NoSQL databases have the following advantages:
This is a grouping of low-cardinality attributes like indicators and flags, removed from other tables, and subsequently “junked” into an abstract dimension table. They are often used to initiate Rapidly Changing Dimensions within data warehouses.
No, it won’t, because null error values are never equal. You can put in numerous null values in a column and not generate an error.
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I hope these Data modeling interview questions have given you an idea of the kind of questions can be asked in an interview. So, if you’re intrigued by what you’ve read about data modeling and want to know how to become a data modeler, then you will want to check the article that shows you how to become one.
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