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1. Top results related to what is a primary key in data warehousing modeling?

   • What is a Primary/Foreign Key Field in this Database Model?

     Top Answer

     Answered Dec 05, 2020 · 10 votes

     The way I understand it is that these fields are primary keys on the table they are defined on, and at the same time they are foreign keys to other tables.

     menu_courses has the reference to suggested_menus through menu_id and combined with course_number makes the primary key - that is each menu/course number combination is unique and uniquely identifies a row on the table.

     You would normally implement this as a foreign key field on the suggested_menus table.

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     1/5

   • What is the purpose of the [Key] attribute on a Primary Key in an asp.net MVC class?

     Top Answer

     Answered Oct 20, 2016 · 0 votes

     When you run code first and use this:

     public class MyClass{ [Key] public int MyKey { get; set; }}-

     This will create in your database:

     Table >> dbo.MyClass Column >> MyKey IDENTITY NOT NULL

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     2/5

   • What is the primary key in such a table?

     Top Answer

     Answered Apr 08, 2018 · 0 votes

     In a word - yes. There's nothing preventing you having a primary key that's also a foreign key to another table. Id is a great primary key for this table.

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     3/5

   • What is the purpose of setting a key in data.table?

     Top Answer

     Answered Jan 05, 2022 · 134 votes

     In addition to this answer, please refer to the vignettes Secondary indices and auto indexing and Keys and fast binary search based subset as well.

     This issue highlights the other vignettes that we plan to.

     I've updated this answer again (Feb 2016) in light of the new on= feature that allows ad-hoc joins as well. See history for earlier (outdated) answers.

     It does two things:

     • reorders the rows of the data.table DT by the column(s) provided (a, b) by reference, always in increasing order.
     • marks those columns as key columns by setting an attribute called sorted to DT.

     The reordering is both fast (due to data.table's internal radix sorting) and memory efficient (only one extra column of type double is allocated).

     For grouping operations, setkey() was never an absolute requirement. That is, we can perform a cold-by or adhoc-by.

     ## "cold" byrequire(data.table)DT <- data.table(x=rep(1:5, each=2), y=1:10)DT[, mean(y), by=x] # no key is set, order of groups preserved in result

     However, prior to v1.9.6, joins of the form x[i] required key to be set on x. With the new on=

     argument from v1.9.6+, this is not true anymore, and setting keys is therefore not an absolute requirement here as well.

     ## joins using < v1.9.6 setkey(X, a) # absolutely requiredsetkey(Y, a) # not absolutely required as long as 'a' is the first columnX[Y]## joins using v1.9.6+X[Y, on="a"]# or if the column names are x_a and y_a respectivelyX[Y, on=c("x_a" = "y_a")]

     Note that on= argument can be explicitly specified even for keyed joins as well.

     The only operation that requires key to be absolutely set is the foverlaps() function. But we are working on some more features which when done would remove this requirement.

     ## compare setkey(X, a, b) # why physically reorder X to just add/update a column? X[Y, col := i.val] ## to X[Y, col := i.val, on=c("a", "b")]

     • So what's the reason for implementing on= argument?There are quite a few reasons. It allows to clearly distinguish the operation as an operation involving two data.tables. Just doing X[Y] does not distinguish this as well, although it could be clear by naming the variables appropriately.
     • It also allows to understand the columns on which the join/subset is being performed immediately by looking at that line of code (and not having to traceback to the corresponding setkey() line).
     • In operations where columns are added or updated by reference, on= operations are much more performant as it doesn't need the entire data.table to be reordered just to add/update column(s). For example, ## compare setkey(X, a, b) # why physically reorder X to just add/update a column? X[Y, col := i.val] ## to X[Y, col := i.val, on=c("a", "b")]In the second case, we did not have to reorder. It's not computing the order that's time consuming, but physically reordering the data.table in RAM, and by avoiding it, we retain the original order, and it is also performant.
     • Even otherwise, unless you're performing joins repetitively, there should be no noticeable performance difference between a keyed and ad-hoc joins.

     This leads to the question, what advantage does keying a data.table have anymore?

     • Is there an advantage to keying a data.table?Keying a data.table physically reorders it based on those column(s) in RAM. Computing the order is not usually the time consuming part, rather the reordering itself. However, once we've the data sorted in RAM, the rows belonging to the same group are all contiguous in RAM, and is therefore very cache efficient. It's the sortedness that speeds up operations on keyed data.tables.It is therefore essential to figure out if the time spent on reordering the entire data.table is worth the time to do a cache-efficient join/aggregation. Usually, unless there are repetitive grouping / join operations being performed on the same keyed data.table, there should not be a noticeable difference.

     In most cases therefore, there shouldn't be a need to set keys anymore. We recommend using on= wherever possible, unless setting key has a dramatic improvement in performance that you'd like to exploit.

     Question: What do you think would be the performance like in comparison to a keyed join, if you use setorder() to reorder the data.table and use on=? If you've followed thus far, you should be able to figure it out :-).

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     4/5

   • What is Meta Data in Data Warehousing?

     Metadata is data that describes and contextualizes other data. It provides information about the content, format, structure, and other characteristics of data, and can be used to improve the organization, discoverability, and accessibility of data. 

     Metadata can be stored in various forms, such as text, XML, or RDF, and can be organized using metadata standards and schemas. There are many metadata standards that have been developed to facilitate the creation and management of metadata, such as Dublin Core, schema.org, and the Metadata Encoding and Transmission Standard (METS). Metadata schemas define the structure and format of metadata and provide a consistent framework for organizing and describing data.

     Metadata can be used in a variety of contexts, such as libraries, museums, archives, and online platforms. It can be used to improve the discoverability and ranking of content in search engines and to provide context and additional information about search results. Metadata can also support data governance by providing information about the ownership, use, and access controls of data, and can facilitate interoperability by providing information about the content, format, and structure of data, and by enabling the exchange of...

     • File metadata: This includes information about a file, such as its name, size, type, and creation date.
     • Image metadata: This includes information about an image, such as its resolution, color depth, and camera settings.
     • Music metadata: This includes information about a piece of music, such as its title, artist, album, and genre.
     • Video metadata: This includes information about a video, such as its length, resolution, and frame rate.
     • Document metadata: This includes information about a document, such as its author, title, and creation date.
     • Database metadata: This includes information about a database, such as its structure, tables, and fields.
     • Web metadata: This includes information about a web page, such as its title, keywords, and description.
     • Descriptive metadata: This type of metadata provides information about the content, structure, and format of data, and may include elements such as title, author, subject, and keywords. Descriptive metadata helps to identify and describe the content of data and can be used to improve the discoverability of data through search engines and other tools.
     • Administrative metadata: This type of metadata provides information about the management and technical characteristics of data, and may include elements such as file format, size, and creation date. Administrative metadata helps to manage and maintain data over time and can be used to support data governance and preservation.
     • Structural metadata: This type of metadata provides information about the relationships and organization of data, and may include elements such as links, tables of contents, and indices. Structural metadata helps to organize and connect data and can be used to facilitate the navigation and discovery of data.
     • Provenance metadata: This type of metadata provides information about the history and origin of data, and may include elements such as the creator, date of creation, and sources of data. Provenance metadata helps to provide context and credibility to data and can be used to support data governance and preservation.
     • Rights metadata: This type of metadata provides information about the ownership, licensing, and access controls of data, and may include elements such as copyright, permissions, and terms of use. Rights metadata helps to manage and protect the intellectual property rights of data and can be used to support data governance and compliance.
     • Educational metadata: This type of metadata provides information about the educational value and learning objectives of data, and may include elements such as learning outcomes, educational levels, and competencies. Educational metadata can be used to support the discovery and use of educational resources, and to support the design and evaluation of learning environments.
     • Improved data quality: A metadata repository can help ensure that metadata is consistently structured and accurate, which can improve the overall quality of the data.
     • Increased data accessibility: A metadata repository can make it easier for users to access and understand the data, by providing context and information about the data.
     • Enhanced data integration: A metadata repository can facilitate data integration by providing a common place to store and manage metadata from multiple sources.
     • Improved data governance: A metadata repository can help enforce metadata standards and policies, making it easier to ensure that data is being used and managed appropriately.
     • Enhanced data security: A metadata repository can help protect the privacy and security of metadata, by providing controls to restrict access to sensitive or confidential information.
     • Lack of standardization: Different organizations or systems may use different standards or conventions for metadata, which can make it difficult to effectively manage metadata across different sources.
     • Data quality: Poorly structured or incorrect metadata can lead to problems with data quality, making it more difficult to use and understand the data.
     • Data integration: When integrating data from multiple sources, it can be challenging to ensure that the metadata is consistent and aligned across the different sources.
     • Data governance: Establishing and enforcing metadata standards and policies can be difficult, especially in large organizations with multiple stakeholders.
     • Data security: Ensuring the security and privacy of metadata can be a challenge, especially when working with sensitive or confidential information.
     • SAP Power Designer by SAP: This data management system has a good level of stability. It is recognised for its ability to serve as a platform for model testing.
     • SAP Information Steward by SAP: This solution’s data insights make it valuable.
     • IBM InfoSphere Information Governance Catalog by IBM: The ability to use Open IGC to build unique assets and data lineages is a key feature of this system.
     • Alation Data Catalog by Alation: This provides a user-friendly, intuitive interface. It is valued for the queries it can publish in Standard Query Language (SQL).
     • Informatica Enterprise Data Catalog by Informatica: The technology used by this solution, which can both scan and gather information from diverse sources, is highly respected.

     Check all answers on Geeksforgeeks

     5/5

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2. medium.com › @felipe › keys-in-dataKeys in Data Warehouse Modeling: A Comprehensive Guide

   medium.com › @felipe › keys-in-data

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   Sep 19, 2023 · Here are key points to understand about primary keys in data warehousing modeling: Uniqueness: Every value in the primary key column must be unique within the table. Non-null: A primary key column ...

3. Videos

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     Data Modeling Tutorial | Data Modeling for Data Warehousing | Data Warehousing Tutorial | Edureka

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     Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Warehousing | Edureka

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4. www.geeksforgeeks.org › types-of-keys-in-dataTypes of Keys in Data Warehouse Schema - GeeksforGeeks

   www.geeksforgeeks.org › types-of-keys-in-data

   • Cached

   • Primary Key – Dimension table’s every row is identified by a unique value which is generally known as primary key of the table. Extraordinary type of one of a kind limitation which can be utilized as the essential method to recover an interesting record from the table.
   • Surrogate Key – These are the keys which are generated by the system and generally does not have any built in meaning. It is UNIQUE since it is consecutively created number for each record being embedded in the table.
   • Foreign Key – In the fact table the primary key of other dimension table is act as the foreign key.
   • Alternate key – It is also a unique value of the table and generally knows as secondary key of the table.

5. medium.com › nerd-for-tech › fundamental-dataFundamental Data modeling and Data warehousing | by Arif ...

   medium.com › nerd-for-tech › fundamental-data

   • Cached

   Feb 27, 2021 · star vs snowflake. Database keys for dimensional modelling. There are 2 type of key that used in dimensional modelling: Primary key: a column that is used to ensure data in the table is unique ...

6. gchandra.gitbook.io › data-warehousing › dataKeys | Data Warehousing - GitBook

   gchandra.gitbook.io › data-warehousing › data

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   • Super Key. Super Key is a set of one or more columns (attributes) to identify rows in a table uniquely. For the above table, a bag of keys will help us uniquely identify that table's rows.
   • Candidate Key. "Minimal super keys" with no redundant attributes to uniquely identify a row. Here the essential condition is Minimal. Let's see what minimal keys help us identify a row uniquely.
   • Primary Key. The primary key is selected from the sets of candidate keys. Let's choose the best column as Primary Key through the process of elimination.
   • Alternate Key. A candidate key should have been selected as Primary Key. Email is best suited for Alternate Key. Email can be used to search particular employees if the system doesn’t have access to EmpNum.

7. learn.microsoft.com › en-us › fabricDefine relationships in data models for data warehousing ...

   learn.microsoft.com › en-us › fabric

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   • Overview
   • Warehouse modeling
   • Next step

   Applies to: SQL analytics endpoint and Warehouse in Microsoft Fabric

   A well-defined data model is instrumental in driving your analytics and reporting experiences. In a Warehouse in Microsoft Fabric, you can easily build and change your data model with a few simple steps in our visual editor. You need to have at least a small sample of data loaded before you can explore these concepts further; tables can be empty, but the schemas (their structures) need to be defined.

   See full list on learn.microsoft.com

   Modeling the warehouse is possible by setting primary and foreign key constraints and setting identity columns on the model view within the data warehouse user interface. After you navigate the model view, you can do this in a visual entity relationship diagram that allows a user to drag and drop tables to infer how the objects relate to one anothe...

   See full list on learn.microsoft.com

   Model data in the default Power BI semantic model in Microsoft Fabric

   See full list on learn.microsoft.com

8. ia.acs.org.au › article › 2020Data modelling: What exactly is a Business Key ...

   ia.acs.org.au › article › 2020

   • Cached

   Nov 19, 2020 · A Business Key satisfying the four conditions above can serve as the Primary Key (PK) for the entity/table. It is just because of this that the BK can act in Foreign Key (FK) relationships with all other tables, since, by definition, the FK must be the PK of another table. A Surrogate Key is a system generated hash key or UUID.

9. People also ask

   What are primary keys in data warehousing modeling?

   • Here are key points to understand about primary keys in data warehousing modeling: Uniqueness: Every value in the primary key column must be unique within the table. Non-null: A primary key column cannot contain NULL values. Stability: Ideally, primary keys should be stable, meaning their values should rarely change.

   Keys in Data Warehousing Modeling: A Comprehensive Guide

   medium.com/@felipe.ramires.terrazas/keys-in-data-warehousing-modeling-a-comprehensive-guide-1901e2162d89

   See all results for this question

   What is data warehousing modeling?

   • Data warehousing modeling is the process of designing the structure and relationships of data within a data warehouse. This modeling enables businesses to organize data for efficient retrieval and analysis. Four fundamental concepts in this modeling process are primary keys and foreign keys, surrogate keys and natural keys.

   Keys in Data Warehousing Modeling: A Comprehensive Guide

   medium.com/@felipe.ramires.terrazas/keys-in-data-warehousing-modeling-a-comprehensive-guide-1901e2162d89

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   What is a foreign key in data warehousing modeling?

   • A foreign key is a column or set of columns in one table that refers to the primary key in another table. Foreign keys establish relationships between tables, enabling data integrity and consistency. Here's what you need to know about foreign keys in data warehousing modeling:

   Keys in Data Warehousing Modeling: A Comprehensive Guide

   medium.com/@felipe.ramires.terrazas/keys-in-data-warehousing-modeling-a-comprehensive-guide-1901e2162d89

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   What type of key is used in dimensional modelling?

   • There are 2 type of key that used in dimensional modelling: Primary key: a column that is used to ensure data in the table is unique. Foreign key: a column or group of columns in a relational database table that provides a link between data in two tables. There are 2 type of key used for primary key in modelling data warehouse:

   Fundamental Data modeling and Data warehousing - Medium

   medium.com/nerd-for-tech/fundamental-data-modeling-and-data-warehousing-b599183d998a

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10. www.databricks.com › blog › data-modeling-bestData Modeling Best Practices & Implementation on Modern ...

    www.databricks.com › blog › data-modeling-best

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    Oct 20, 2022 · Primary and Foreign Key definitions are very important when creating a data model. Having the ability to support the PK/FK definition makes defining the data model super easy in Databricks. It also helps analysts quickly figure out the join relationships in Databricks SQL Warehouse so that they can effectively write queries.