How deep to go when denormalising - data-warehouse

I denormalising a OLTP database for use in a DWH.
At the moment I am denormalising studygroups.
Each studygroup has a key pointing towards 1 project.
Each project has a key pointing towards 1 department.
Each department has a key pointing towards 1 university.
Each universityhas a key pointing to 1 city.
Now I know that you are supposed to denormalize the sh*t out your OLTP but in this dwh department will be a dimension on its own. This goes for university also. Would it suffise to add a key from studygroup pointing at department or is it wiser to denormalize as far as you can and add all attributes from the department and all attributes from its M:1 related tables to the dimension studygroup? Even when department and university will be dimensions by themselves?
In other words: how far/deep do you go when denormalizing?

The key concept behind a dimensional model is:
Keep your fact tables in 3NF (third normal form);
De-normalize your dimensions into 2NF (second normal form)
So ideally, the only joins you should have in your model are the joins between fact tables and relevant dimensions.
As part of this philosophy:
Avoid "snow flake" designs, where dimensions contain keys to other dimensions. It's always possible to come up with a data model that allows the same functionality as the snow flakes, without violating 3NF/2NF rule;
Never have any direct joins between 2 separate dimensions (i.e, department and study group) directly. All relations among dimensions must be resolved via fact tables;
Never have any direct joins between 2 separate fact tables. Any relations among fact tables must be resolved via shared dimensions.
Finally, consider that dimensional design, besides optimization of the data for querying, serves a second important purpose: it's a semantic model of the business (or whatever else it represents). So, when making decisions about combining data elements into dimensions and facts, consider their "logical affinity" - they should make intuitive sense to the end users. If you have hard times explaining to a BI analyst the meaning of your dimension or fact table, most likely you've made a modeling mistake.
For example, in your case you should consider logical relations between universities, departments, study groups, etc. It's very likely that University/Department form a natural hierarchy. If so, they should belong to the same dimension. Study group, on the other hand, might not - let's assume, it's possible to form study groups across multiple universities and/or multiple departments. Such Many:Many relations are clear indication that they should be resolved via fact tables. In addition, relations between universities and departments are stable (rarely change), while study groups are formed and dissolved very often, and thus should be modeled separately.
In general, if you see 1:1 or 1:M relations between dimensional elements, it's often an indication that they should be de-normalized into the same table (again, only if their combination makes logical sense). If the relations are M:M, most likely they belong to different tables (you can force them into the same table, but often such tables look like Frankenstein creatures).
You can get much better help by making your question more specific - draw your dimensional model, post it, and ask for specific issues/challenges you have. For general concepts, books from Kimball and Inmon are your best friends.

Related

Material and Product hierarchies

I have a master data with both the material and product details in a single table. I am creating a star schema and my question is do i need to make two dimension table with separate material attributes and product attributes or can i have both in a single dimension table? The current master data looks has the following fields -
Material id, name, type, product hier 1,2,3,4...product hierarchy, product category, sub category. In my case both material and product are same, so a single id.
I am thinking of making it in a single table, but is that the best practice? Any future potential issues?
Many thanks in advance,
Arun
The important (and obvious) thing is, that the fact table has two separate foreign keys: PRODUCT_ID and MATERIAL_ID, both referencing your single dimension table.
This setup is not always best practice for OLTP systems, because in this case the database can't enforce the referential integrity. (You may store a product ID in the MATERIAL_ID column).
But for data-warehouse the database constraints are typically not enabled and are enforced in the loading job, so this setup is fine.
The decision to split is more dependent on the origin of the two dimensions. If both of them are maintained together, I see no reason to split them. If the two dimension are independent, with different lifecycles and separate sources, there is no reason to combine them.
And BTW Kimball IMO mentions the split of hierarch levels (not separate dimensions). So he sees as an mistake to split the product attributes and the hiearchy and category attributes (which is not your problem).
It depends on your business requirement.
If you ever need to produce a report that shows (say) units produced of product category by material, then you need to keep them in separate dimensions.

Fact table linked to Slowly Changing Dimension

I'm struggling to understand the best way to model a particular scenario for a data warehouse.
I have a Person dimension, and a Tenancy dimension. A person could be on 0, 1 or (rarely) multiple tenancies at any one time, and will often have a succession of tenancies over time. A tenancy could have one or more people associated with it. The people associated with a tenancy can change over time, and tenancies generally last for many years.
One option is to add tenancy reference, start and end dates to the Person Dimension as type 2 SCD columns. This would work well as long as I ignore the possibility of multiple concurrent tenancies for a person. However, I have other areas of the data warehouse where I am facing a similar design issue and ignoring multiple relationships is not a possibility.
Another option is to model the relationship as an accumulating snapshot fact table. I'm not sure how well this would work in practice though as I could only link it to one version of a Person and Tenancy (both of which will have type 2 SCD columns) and that would seem to make it impossible to produce current or historical reports that link people and tenancies together.
Are there any recommended ways of modelling this type of relationship?
Edit based on the patient answer and comments given by SQL.Injection
I've produced a basic model showing the model as described by SQL.Injection.
I've moved tenancy start/end dates to the 'junk' dimension (Dim.Tenancy) and added Person tenancy start/end dates to the fact table as I felt that was a more accurate way to describe the relationship.
However, now that I see it visually I don't think that this is fundamentally any different from the model that I started with, other than the fact table is a periodic snapshot rather than an accumulating snapshot. It certainly seems to suffer from the same flaw that whenever I update a type 2 slowly changing attribute in any of the dimensions it is not reflected in the fact.
In order to make this work to reflect current changes and also allow historical reporting it seems that I will have to add a row to the fact table every time a SCD2 change occurs on any of the dimensions. Then, in order to prevent over-counting by joining to multiple versions of the same entity I will also need to add new versions of the other related dimensions so that I have new keys to join on.
I need to think about this some more. I'm beginning to think that the database model is right and that it's my understanding of how the model will be used that is wrong.
In the meantime any comments or suggestions are welcome!
Your problem is similar to to the sale transactions with multiple item. The difference, is that a transaction usually has multiple items and your tenancy fact usually has a single person (the tenant).
Your hydra is born because you are trying to model the tenancy as a dimension, when you should be modeling it as a fact.
The reason why I think you have a tenancy dimension, is because somewhere you have a fact rent. To model the fact rent consider use the same approach i stated above, if two persons are tenants of the same property two fact records should be inserted each month:
1) And now comes some magic (that is no magic at all), split the value of the of the rent by the number of tenants and store it the fact
2) store also the full value of the rent (you don't know how the data scientist is going to use the data)
3) check 1) with the business user (i mean people that build the risk models); there might be some advanced rule on how to do the spliting (a similar thing happens when the cost of shipping is to be divided across multiple item lines of the same order -- it might not be uniformly distributed)

Bitemporal master-satellite relationship

I am a DB newbie to the bitemporal world and had a naive question.
Say you have a master-satellite relationship between two tables - where the master stores essential information and the satellite stores information that is relevant to only few of the records of the master table. Example would be 'trade' as a master table and 'trade_support' as the satellite table where 'trade_support' will only house supporting information for non-electronic trades (which will be a small minority).
In a non-bitemporal landscape, we would model it as a parent-child relationship. My question is: in a bitemporal world, should such a use case be still modeled as a two-table parent-child relationship with 4 temporal columns on both tables? I don't see a reason why it can't be done, but the question of "should it be done" is quite hazy in my mind. Any gurus to help me out with the rationale behind the choice?
Pros:
Normalization
Cons:
Additional table and temporal columns to maintain and manage via DAO's
Defining performant join conditions
I believe this should be a pretty common use-case and wanted to know if there are any best practices that I can benefit from.
Thanks in advance!
Bitemporal data management and foreign keys can be quite tricky. For a master-satellite relationship between bitemporal tables, an "artificial key" needs to be introduced in the master table that is not unique but identical for different temporal or historical versions of an object. This key is referenced from the satellite. When joining the two tables a bitemporal context (T_TIME, V_TIME) where T_TIME is the transaction time and V_TIME is the valid time must be given for the join. The join would be something like the following:
SELECT m.*, s.*
FROM master m
LEFT JOIN satellite s
ON m.key = s.master_key
AND <V_TIME> between s.valid_from and s.valid_to
AND <T_TIME> between s.t_from and s.t_to
WHERE <V_TIME> between m.valid_from and m.valid_to
AND <T_TIME> between m.t_from and m.t_to
In this query the valid period is given by the columns valid_from and valid_to and the transaction period is given by the columns t_from and t_to for both the master and the satellite table. The artificial key in the master is given by the column m.key and the reference to this key by s.master_key. A left outer join is used to retrieve also those entries of the master table for which there is no corresponding entry in the satellite table.
As you have noted above, this join condition is likely to be slow.
On the other hand this layout may be more space efficient in that if only the master data (in able trade) or only the satellite data (in table trade_support) is updated, this will only require a new entry in the respective table. When using one table for all data, a new entry for all columns in the combined table would be necessary. Also you will end up with a table with many null values.
So the question you are asking boils down to a trade-off between space requirements and concise code. The amount of space you are sacrificing with the single-table solution depends on the number of columns of your satellite table. I would probably go for the single-table solution, since it is much easier to understand.
If you have any chance to switch database technology, a document oriented database might make more sense. I have written a prototype of a bitemporal scala layer based on mongodb, which is available here:
https://github.com/1123/bitemporaldb
This will allow you to work without joins, and with a more flexible structure of your trade data.

Performance issues with complex nested RoR reservation system

I'm designing a Ruby on Rails reservation system for our small tour agency. It needs to accommodate a number of things, and the table structure is becoming quite complex.
Has anyone encountered a similar problem before? What sort of issues might I come up against? And are performance/ validation likely to become issues?
In simple terms, I have a customer table, and a reservations table. When a customer contacts us with an enquiry, a reservation is set up, and related information added (e.g., paid/ invoiced, transport required, hotel required, etc).
So far so good, but this is where is gets complex. Under each reservation, a customer can book different packages (e.g. day trip, long tour, training course). These are sufficiently different, require specific information, and are limited in number, such that I feel they should each have a different model.
Also, a customer may have several people in his party. This would result in links between the customer table and the reservation table, as well as between the customer table and the package tables.
So, if customer A were to make a booking for a long trip for customers A,B and C, and a training course for customer B, it would look something like this.
CUSTOMERS TABLE
CustomerA
CustomerB
CustomerC
CustomerD
CustomerE
etc
RESERVATIONS TABLE
1. CustomerA
LONG TRIP BOOKINGS
CustomerA - Reservation_ID 1
CustomerB - Reservation_ID 1
CustomerC - Reservation_ID 1
TRAINING COURSE BOOKINGS
CustomerB - Reservation_ID 1
This is a very simplified example, and omits some detail. For example, there would be a model containing details of training courses, a model containing details of long trips, a model containing long trip schedules, etc. But this detail shouldn't affect my question.
What I'd like to know is:
1) are there any issues I should be aware of in linking the customer table to the reservations model, as well as to bookings models nested under reservations.
2) is this the best approach if I need to handle information about the reservation itself (including invoicing), as well as about the specific package bookings.
On the one hand this approach seems to be complex, but on the other, simplifying everything into a single package model does not appear to provide enough flexibility.
Please let me know if I haven't explained this issue very clearly, I'm happy to provide more information. Grateful for any ideas, suggestions or comments that would help me think through this rather complex database design.
Many thanks!
I have built a large reservation system for travel operators and wholesalers, and I can tell you that it isn't easy. There seems to be similarity yet still large differences in the kinds of product booked. Also, date-sensitivity is a large difference from other systems.
1) In respect to 'customers' I have typically used different models for representing different concepts. You really have:
a. Person / Company paying for the booking
b. Contact person for emergencies
c. People travelling
a & b seem like the same, but if you have an agent booking, then you might want to separate them.
I typically use a => 'customer' table, then some simple contact-fields for b, and finally for c use a 'passengers' table. These could be setup as different associations to the same model, but I think they are different enough, and I tend to separate them - perhaps use a common address/contact model.
2) I think this is fine, but depends on your needs. If you are building up itineraries for a traveller, then it makes sense to setup 'passengers' on the 'reservation', then for individual itinerary items, with links to which passenger is travelling on/using that item.
This is more complicated, and you must be careful to track dependencies, but the alternative is to not track passenger names, and simply assign quantities to each item (1xAdult, 2xChildren). This later method is great for small bookings, so it seems to depend on if your bookings are simple, or typically built up of longer itineraries.
other) In addition, in respect to different models for different product types, this can work well. However, there tends to be a lot of cross over, so some kind of common 'resource' model might be better -- or some other means of capturing common behaviour.
If I haven't answered your questions, please do ask more specific database design questions, or I can add more detail about specific examples of what I've found works well.
Good luck with the design!

Modeling a database (ERD) that has quirky behavior

One of the databases that I'm working on has some quirky behavior that I want to account for in the entity-relationship diagram.
One of the behaviors is that there is a 'booking' table and a 'invoice' table. When a 'booking' is invoiced, then the record is inserted into the 'invoice' table and then deleted from the 'booking' table.
However, a reference is still kept of the booking number.
How do we model this? Big arrow between the tables and some text beside it describing what happens?
No, changing the database schema is not possible at this point in time
Edit: This is the type of diagram that I want to use:
alt text http://img813.imageshack.us/img813/5601/erdartistperformssong.png
Link
If, by ERD, you mean the original "Chen" diagrams where the relationship was words written in a diamond, then you have a relationship between between Booking and Invoice. It's a special kind of relationship that's NOT implemented with a simple foreign key; it's implemented via a complicated move and a constraint.
If, by ERD, you mean the diagrams that ERwin draws, then you don't have an easy way to do this. It tends to focus you on drawing PK-FK relationships. You have a non-PK-FK relationship between these things. Some kind of line with text is about all you can do.
Arrows, BTW, aren't appropriate because the ERD shows the "state" of the database. Data flowing around isn't part of an ERD. You do have a relationship, it's just not a typical PK-FK relationship. It's an atypical relationship based on rows existing in some places and not existing in others.
In the UML you can easily draw this as a "constraint" among the relationships.
I don't know what these people are talking about.
The Entity Relation Diagram doesn't describe the data fully; yes of course, it only shows Entities and Relations, it doesn't show Attributes. That's why it is called an ERD and not a Data Model. Evidently many people here can't tell the difference.
The Data Model is supposed to show as much as possible. But it depends on (a) the standard [if any] that you use and (b) the Notation. Some show more than others. IDEF1X which is the only Relational modelling Standard (NIST 184 of 1993). It is the most complete, and shows intricacies and complexities that other notations do not show. Recently MS and others have come out with "simplified" notations, of course, much is lost in the "ERDs".
It is not "process flow", it is a relation in a database.
UML is completely inappropriate for modelling data, especially when there is at least one Standard plus several non-standard but commonly used data modelling notations. There is nothing that can be shown in UML that can't be shown in IDEF1X. But most developers here have never heard of it (developers should not be modelling unless they have acquired modelling skills, but that is another story)..
This is a perfectly legal; it may not be commonly known, but it is legal and named. It is a Supertype-Subtype relation, except that the Cardinality is 1::0-n instead of 1::0-1. The IDEF1X Notation (right) has a Subtype symbol. Note there is only one relation at the parent end; and one each at the child end. And of course the crows feet show the cardinality. These relations can be Exclusive or Non-exclusive; yours is Exclusive; that is what the X through the half-circle means.
ERwin is the only modelling (not diagramming) tool that implements IDEF1X, and thus has the full complement of the IDEF1X Notation.
Of course, the Standard, the modelling capability, are all in the mind, not in the tool. I draw Data Models that are IDEF1X-compliant using a simple drawing tool.
I find that some developers baulk at the Subtype symbol, so I show a simplified version (left) in my IDEF1X models; it is intended to convey the sense of exclusivity, while the retention of the single line at the parent end indicates it is a subtype.
Lott: Click here▶Link to Data Model◀Lott: Click here
Link to IDEF1X Notation for those who are unfamiliar with the Relational Modelling Standard.
Sounds like a process flow, not an entity relationship. If at the time the entry is added to invoice, and the entry is deleted from booking, then there is never a relationship between the two. There is never a situation where you can traverse that relationship because there is never a record in both places that can be related together.
ERD don't describe the database fully. There are other things like process flow and use cases that detail other facets of the system.
This is kind of an analogy to UML for software. A class diagram doesn't show you all the different ways classes interact. One class might initialize locally and call functions of another class, but because there is not composition or inheritance that relates those two classes, then the class diagram doesn't show this relationship. Only when you fully document the system with all the various types of diagrams can you see all the facets of how it operates.

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