What Are The Different Critical Relationship Types In A Data Model
A relationship mainly connects parent and child tables. In a data model, there are three types of critical relationships:
Identifying: In this type, the primary keys of the parent tables include a reference column of child tables, which helps identify the entries. A thick line often replaces this relationship. Because the foreign key forms part of the primary key, they are referred to as identifying keys. There can’t be a child object if there isn’t a parent object.
Non-Identifying: A child table’s reference field does not exist in the parent table’s primary key. A dotted line represents this relationship. Depending on the situation, this relationship can be optional or mandatory. It signifies that NULL can be allowed or not allowed in foreign key columns.
Self-Recursive: In this case, a single column links to a table’s primary key. This relationship exists between different column objects belonging to a similar entity.
Briefly Define Factless Fact Tables In Data Modeling
A factless fact table does not include any facts. They only have dimensional keys and capture events that occur only at the information level, not at the computation level . A factless fact table holds the many-to-many links between dimensions and contains no numeric or textual facts. They’re commonly used to document events or information about coverage.
Factless fact tables help track a process or collect data. There are two types of factless fact tables: one that describes occurrences and one that describes conditions.
What Is Data Modeling
Data modeling is creating data models to store in a database. It is a conceptual representation of data objects, the association between different data objects, and the rules. It also represents how the data flows. In other words, data modeling is creating a simplified diagram that contains data elements in the form of texts and symbols.
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What Are The Advantages Of Data Model
Advantages of the data model are:
- The main goal of a designing data model is to make sure that data objects offered by the functional team are represented accurately.
- The data model should be detailed enough to be used for building the physical database.
- The information in the data model can be used for defining the relationship between tables, primary and foreign keys, and stored procedures.
- Data Model helps businesses to communicate within and across organizations.
- Data model helps to documents data mappings in the ETL process
- Help to recognize correct sources of data to populate the model
Q3 What Are A Logical Data Model And Logical Data Modeling
A logical data model is the version of a data model that represents the business requirements . This is the actual implementation and extension of a conceptual data model.
Logical Data Models contain Entity, Attributes, Super Type, Sub Type, Primary Key, Alternate Key, Inversion Key Entry, Rule, Relationship, Definition, etc. The approach by which logical data models are created is called logical data modeling.
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Mention The Different Types Of Measures In Data Modelling
There are three types of measures in data modeling. Non-addictive measures do not allow any application of aggregation function on top of it. A good example is a ratio, percentage of indicator column in a given fact table. semi-additive measures allow the application of some aggregate functions on top. However, some do not make the cut. Lastly, additive measures allow the application of all aggregation functions on top.
Differentiate Sql And Nosql
SQL and NoSQL differ in flexibility, models, data types, and transactions. SQL has a relational data model, deals with structured data, and follows a strict schema and ACID properties in their transactions which fully means atomicity, consistency, isolation, and durability. On the other hand, NoSQL has non-relational data models that deal with semi-structured data and dynamic schema, making them very flexible. They follow BASE properties. BASE fully means basic availability, soft state, and eventual consistency.
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Mention Some Of The Fundamental Data Models
Fully-Attributed : This is a third normal form model that provides all the data for a specific implementation approach.
Transformation Model : Specifies the transformation of a relational model into a suitable structure for the DBMS in use. The TM is no longer in the third normal form in most cases. The structures are optimized depending on the DBMSs capabilities, data levels, and projected data access patterns the structures are optimized.
DBMS Model: The DBMS Model contains the database design for the system. The DBMS Model can be at the project or area level for the complete integrated system.
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What Is The Function Of Amazon Redshift
Amazon Redshift is a cloud-based data warehousing solution that is quick, fully managed, and extends to petabytes.
It enables you to examine your data quickly and effectively using your existing business intelligence tools.
What Do You Mean By The Cap Theorem How Does It Work
The CAP theorem shows that no distributed system can ensure C, A, and P at the same time. It states that a distributed system cannot deliver over two of the three assurances.
Consistency: After an activity, the data should stay consistent. After upgrading the database, for example, all the queries should return the same result.
Availability: There should be no downtime with the database it should always be accessible and active.
Partition Tolerance: The system should remain functional even if communication between the servers is inconsistent.
Q44 What Is The Third Normal Form
An entity is in the third normal form if it is in the second normal form and all of its attributes are not transitively dependent on the primary key. Transitive dependence means that descriptor key attributes depend not only on the whole primary key but also on other descriptor key attributes that, in turn, depend on the primary key.
In SQL terms, the third normal form means that no column within a table is dependent on a descriptor column that, in turn, depends on the primary key.
For 3NF, first, the table must be in 2NF, plus, we want to make sure that the non-key fields are dependent upon ONLY the PK, and not other non-key fields for its existence. This is very similar to 2NF, except that now you are comparing the non-key fields to OTHER non-key fields. After all, we know that the relationship to the PK is good because we established that in 2NF.
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About Erwin Data Modeler Test
Erwin data modeler screening test may contain MCQs , MAQs , Fill in the Blanks, Whiteboard Questions, Audio/Video Questions, LogicBox , Job-based Simulations, True or False Questions, etc.Erwin data modeler competency test is designed with consideration of EEOC guidelines for candidate assessment. It will help you to assess and hire diverse talent without any bias.
Briefly Define Normalization And The Three Normal Forms
In relational database design, normalization organizes data in a relational structure to reduce redundancy and non-relational structures. By deleting any model structures that allow various methods to know the same information, you can control and eliminate data redundancy by following the normalization criteria.
First Normal Form – The entity E is in first normal form if and only if all underlying values include only atomic values. You must remove recurring sets .
Second Normal Form – If an entity E is in 1NF and every non-key attribute depends entirely on the primary key, it is in 2NF.
Third Normal Form – If an entity E is in 2NF and no non-key attribute of E depends on another non-key attribute, it is in 3NF. Another way to think about it is that if an entity E is in 2NF and every non-key attribute is non-transitive, relying on the primary key, it is in 3NF.
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Data Modelling Training Online
72. Define a Second Normal form .
Ans. In case all the attributes/elements depend only on the primary key, then an entity is considered to be in the 2nf. In declarative terms, each column within a table must be functionally dependent on the entire primary key of the same table. This dependence specifies that there is a connection between two different column values.
73. What is meant by Granularity?
Ans. The term Granularity means the volume of information that a table carries. It can be measured in types – High or Low. A low granularity contains only low-level or low-volume information whereas high granularity data include transaction-level data.
74. All databases must be in the Third normal form- True or False?
Ans. Usually, all enterprise databases are normalized to the Third Normal Form to pull out redundancy and efficient access. We can also develop a database without
Define Conformed Dimension.
normalization. Therefore, it’s not necessary that all databases should be in the Third Normal Form.
75. Define Conformed Dimension.
Ans. A dimension is considered confirmed if the same is attached to two fact tables. Examples of conformed dimensions would include calculating profits, revenue, price, margin, cost, etc.
76. What are Dimensions in data?
Ans. The dimensions in the data are a group of unique values useful for locating and classifying data from the data storage or warehouse.
77. What do you mean by Data Mart?
- Business Metadata
- Reference Metadata
What Do You Understand By Data Modelling
It is creating a data model representing a given dataset and its relationship to other data for storage purposes. Data modeling is also referred to as database modeling. It comes in handy in different data-related jobs such as data engineering, software development, data science, and any other job that requires data to be prepared, analyzed, and processed. Data modeling helps reorganize, optimize and reorganize data to fit different needs of a company or an organization. A data modeling project results are data models categorized into conceptual, physical, and logical models.
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Can You Compare And Contrast Star Schema And Snowflake Schema
The way this question is answered will show your strong understanding of data organization. The data schema of a database is its structure described in formal language supported by the management system.
Schema refers to a blueprint of how that data is constructed. Star schema contains a single fact table surrounded by dimension tables.
Snowflake schema typically stores the same data as the star schema, but the information is structured due to normalization. When answering, indicate when it is appropriate to use each schema.
Example:Star schema and snowflake schema are the most popular multidimensional models used in a data warehouse and are similar in the data they can store but have one main difference: star schema is denormalized and snowflake schema is normalized. I had a client using star schema to analyze data and because of redundancy, it was inaccurate. Central to star schema is a fact table or several fact tables.
These fact tables index a series of dimension tables. Star schema separate fact data, things like price, percentage and weight from dimensional data that includes things like color, names and geographic locations. They function well for quick queries and can provide access to basic information on a wide scale. However, the denormalized structure does not enforce data integrity well despite efforts to prevent anomalies from happening. This aspect made data analysis for my client very difficult.
What Are The Drawbacks Of The Hierarchical Data Model
The drawbacks of the hierarchical data model are:
- It is not flexible as it takes time to adapt to the changing needs of the business.
- The structure poses the issue in, inter-departmental communication, vertical communication, as well as inter-agency communication.
- Hierarchical data model can create problems of disunity.
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Q20 What Are A Conceptual Data Model And Conceptual Data Modeling
The conceptual data model includes all major entities and relationships and does not contain much detailed level of information about attributes and is often used in the initial planning phase. Data Modelers create a conceptual data model and forward that model to the functional team for their review.
The approach by which conceptual data models are created is called conceptual data modeling.
What Errors Have You Faced In Your Work
There are four main errors that I have faced in my data modeling project works. The most common is the creation of excessively broad data models, which occurs when tables are run higher than 200, making the data model too complex. Another common error is a missing purpose due to a lack of knowledge of a businesss goals or missions. Therefore, a data modeler must fully understand the entitys business model. Other errors include unnecessary surrogate keys and inappropriate denormalization, which leads to redundant data that is challenging to maintain.
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Top Data Modeling Interview Questions Asked In 201:
- what is Canonical Data Model?
- What is Optionality?
- How columnar databases are different from the RDBMS Database?
- What are the deliverables of a Data Modeler?
- How do you present the data model to the business team and the technical team?
- How do you maintain the data model after the project implementation?
- How do you define the business rules in the data model?
- Can you do shared role?
- What is Enterprise Data Architecture?
- What is Big Data Data Modeling?
- What kind of knowledge you have in NO SQL Databases?
- What is the difficult scenario you faced in Data Modeling?
- What is the document equivalent to RTM in Data Modeling?
- Which approach you followed: Inmons approach or Ralph Kimballs approach? & Why?
- As a Data Modeler, how can you ensure that the Data is available to customer 24/7, 365 days and the Data is most reliable?
- What is the data modeling tool that you have worked?
- How will you reverse engineer using a data dictionary in excel format?
- Can we reverse engineer, upload an Excel file with entity name, attribute name and datatype to create a data model?
- Why do you have a flat file in Data Warehouse environment without primary keys?
- What is the difference between a domain and a datatype?
- Under which scenario you would use recursive relationship, why and why not?for example: Hierarchical data set or Master-Detail data set?
What Exactly Is A Data Model
A data model organizes and standardizes the relationships between various data items and entity attributes. Data modeling is hence the process of constructing these data models. Entities are the objects and concepts whose data we wish to track, and data models are comprised of entities. In turn, they are transformed into database tables. For example, customers, manufacturers, sellers, and products are all potential entities. Furthermore, each object has characteristics, which are details that users wish to monitor.
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Q33 What Is The Data Model Metadata
You can take a report of the entire data model, or subject, or part of the data model. The data about various objects in the data model is called data model Metadata. Data Modeling Tools have options to create reports by checking various options. Either you can create a logical data model Metadata of physical model Metadata.
In The Context Of Data Modeling Define Factless Fact Tables Briefly
A factless fact table has no information. It only possesses dimensional keys and records events at the information level, not the calculation level . The many-to-many relationships between dimensions are contained in a fact table with no numerical or textual facts. They are typically used to chronicle events or coverage informationinaccurate fact tables aid in monitoring a process or collecting data. One sort of factless fact table explains events, while the other describes conditions.
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What Problems Have You Encountered In Your Work
I have encountered four major problems in my data modeling project work. The most frequent occurrence is the construction of extremely broad data models, which occurs when tables exceed 200 rows, resulting in an overly complicated data model. Unawareness of a companys objectives or aims is a source of a second common error: a lack of focus. Therefore, a data modeler must completely comprehend the firms business model. Other mistakes include unnecessary surrogate keys and incorrect de-normalization, which result in difficult-to-maintain redundant data.
Which Data Models Are Considered To Be The Most Fundamental
- Fully-Attributed : This third normal form model contains all the data necessary for a particular implementation strategy.
- Transformation Model : Specifies the transformation of a relational model into a structure acceptable for the database management system in use. In most cases, the TM is no longer in the third normal form. Depending on the DBMSs capabilities, data levels, and anticipated data access patterns, the structures are optimized the structures are optimized.
- The DBMS Model contains the database architecture for the system. The DBMS Model for the entire integrated system can be at the project or area level.
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What Is The Use Of Erwin Data Modeler
This ERwin tool interview question examines whether you are up to date with the latest software and if you know of its different capabilities. It vets for a modelers ability to normalize a data model and help a client get the most authentic information.
For an effective answer, explain what you find to be its strongest feature or function, what makes it unique and in what capacity you have used it.
Example: In my position as team-lead on our last project, I was introduced to ERwin, a software used for data modeling. Our client needed a solution to lower data management costs. We used it to create an actual database from the physical mode, streamlining the entire process. A bonus to ERwin is that it has options for colors, fonts, layouts and more. However, I found it particularly useful that it can be used to reverse engineer.