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Why do we need Junk Dimension | Data Warehouse Concepts 

aroundBI
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We are going to talk about Junk Dimension- what is junk dimension, why do we create junk dimension.
A junk dimension is a collection of random transaction codes flags and/or text attributes that are unrelated to any particular dimension. By creating a junk dimension, we remove the flags from the fact table and place them into a useful dimensional framework.
Please share your thoughts and feedback.
Thanks for watching.
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29 авг 2024

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Комментарии : 31   
@Ronitkable
@Ronitkable 2 года назад
I would like to correct one thing here is 'Junk Dimension tables contain a surrogate key that fact table uses to reference the row. Basically, fact table contains a foreign key to rows in Junk Dimension table' Your diagram shows surrogate key part of Fact table. Rest, really good and well explained video.
@robertjohnson5838
@robertjohnson5838 3 года назад
At 4:40, While you said "Four indicators that take on 3 values..." and your 3**4 is consistent with that, you actually have 4 in the Values portion and 3 in the Indicators portion, and if THAT were true it would be 4**3 = 64.
@user-wi9mz3gt2e
@user-wi9mz3gt2e 10 месяцев назад
Very clear and logical explanation, thanks!
@329smh
@329smh 7 лет назад
Very well explained..would love to see similar video on "Mini Dimension" as well.
@rahasyagumnam7806
@rahasyagumnam7806 4 года назад
Why can't store type be one of the attributes of store dimension rather than being a junk dimension?
@rammadipeddi5864
@rammadipeddi5864 2 года назад
Can you please do video on interview point of view with most expected questions please
@tariqawan5279
@tariqawan5279 Год назад
Very Nice Explanation. Thanks
@makarandnidhalkar7139
@makarandnidhalkar7139 3 года назад
brilliantly explained. thanks
@travel_the_world252
@travel_the_world252 6 лет назад
Good explanations thank you
@highcontrast4488
@highcontrast4488 Год назад
nice
@AkshuGotuDance
@AkshuGotuDance 3 года назад
Excellent Explanation Thanks
@mohitrock100
@mohitrock100 6 лет назад
Great Tutorial . Very Nicely Explained .
@CevicheChessSalsa
@CevicheChessSalsa 5 лет назад
Very nice explanation, thanks so much!
@gtm6505
@gtm6505 2 года назад
Thanq very informative
@venkateshreddy2137
@venkateshreddy2137 5 лет назад
Thanks for detailed explanation.
@AnilJacobs
@AnilJacobs 5 лет назад
Never thought about this, thanks! How do you maintain them in Datawarehouse? Must we frequently check new values in fact tables at ETL time and keep it updated every time?
@nitagawade3330
@nitagawade3330 4 года назад
Excellent
@roopbasant20031
@roopbasant20031 7 лет назад
Good explanation
@hatemelshesheny814
@hatemelshesheny814 4 года назад
Really helpful video
@pritammishra9850
@pritammishra9850 4 года назад
amazing tutorial
@ziedx5
@ziedx5 6 лет назад
Thanks, nice explanation
@dineshreddy1478
@dineshreddy1478 5 лет назад
Perfect Thanks
@acidforblood
@acidforblood 3 года назад
I'm fuzzy on the definition of indicators in the context of a Junk Dimension. I also lack experience with the terminology. Would indicators be the columns or fields in question that hold the combinations of values?
@shaliniguha1822
@shaliniguha1822 3 года назад
Basically the columns like payment type, store type etc and the unique values are the possible values they can hold.
@getusama
@getusama 3 года назад
please can you show what the Junk Dimension actually look like for the example you have explained?
@hamedhussaini1541
@hamedhussaini1541 3 года назад
I guess it will look like a table with every possible combination of values, I would rather ask how do we go about creating it. Is there any automatic way of creating it or is it going to be a manual process. By the way its been long time, where are you and how have you been? message me personally.
@MyLove-uo3bw
@MyLove-uo3bw 2 года назад
3:25 Definition
@prouser7080
@prouser7080 5 лет назад
meri english khrab ho gyi
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