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.
Related
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.
Considering there is a data warehouse contains one fact table and three dimension tables.
Fact table:
fact_orders
Dimension tables:
dim_user
dim_product
dim_date
All the data of these tables are extracted from our business systems.
In the business system, the user has many attributes, some of which could change upon time(mobile, avatar_url, nick_name, status), some others won't change once the record is created(id,gender,register_channel).
So generally in the dim_user table, which fields should we use and why?
Dim_User should have both changeable and unchangeable fields. In denormalized model, it is preferrable to keep all the related attributes of a dimension in a single table.
Also, it is preferrable to keep all the information available about user in the dimension table, as they might be used for reporting purposes. If they won't be needed for reporting purpose, you can skip them.
If you want to keep the history of change of the user, you can consider implementing slowly changing dimensions. Otherwise, you can update the dimension attributes, as and when they change. It is called SCD Type I.
First project using star schema, still in planning stage. We would appreciate any thoughts and advice on the following problem.
We have a dimension table for "product features used", and the set of features grows and changes over time. Because of the dynamic set of features, we think the features cannot be columns but instead must be rows.
We have a fact table for "user events", and we need to know which product features were used within each event.
So it seems we need to have a primary key on the fact table, which is used as a foreign key within the dimension table (exactly the opposite direction from a conventional star schema). We have several different dimension tables with similar dynamics and therefore a similar need for a foreign key into the fact table.
On the other hand, most of our dimension tables are more conventional and the fact table can just store a foreign key into these conventional dimension tables. We don't like that this means that some joins (many-to-one) will use the dimension table's primary key, but other joins (one-to-many) will use the fact table's primary key. We have considered using the fact table key as a foreign key in all the dimension tables, just for consistency, although the storage requirements increase.
Is there a better way to implement the keys for the "dynamic" dimension tables?
Here's an example that's not exactly what we're doing but similar:
Suppose our app searches for restaurants.
Optional features that a user may specify include price range, minimum star rating, or cuisine. The set of optional features changes over time (for example we may get rid of the option to specify cuisine, and add an option for most popular). For each search that is recorded in the database, the set of features used is fixed.
Each search will be a row in the fact table.
We are currently thinking that we should have a primary key in the fact table, and it should be used as a foreign key in the "features" dimension table. So we'd have:
fact_table(search_id, user_id, metric1, metric2)
feature_dimension_table(feature_id, search_id, feature_attribute1, feature_attribute2)
user_dimension_table(user_id, user_attribute1, user_attribute2)
Alternatively, for consistent joins and ignoring storage requirements for the sake of argument, we could use the fact table's primary key as a foreign key in all the dimension tables:
fact_table(search_id, metric1, metric2) /* no more user_id */
feature_dimension_table(feature_id, search_id, feature_attribute1, feature_attribute2)
user_dimension_table(user_id, search_id, user_attribute1, user_attribute2)
What are the pitfalls with these key schemas? What would be better ways to do it?
You need a Bridge table, it is the recommended solution for many-to-many relationships between fact and dimension.
http://www.kimballgroup.com/data-warehouse-business-intelligence-resources/kimball-techniques/dimensional-modeling-techniques/multivalued-dimension-bridge-table/
Edit after example added to question:
OK, maybe it is not a bridge, the example changes my view.
A fundamental requirement of dimensional modelling is to correctly identify the grain of your fact table. A common example is invoice and line-item, where the grain is usually line-item.
Hypothetical examples are often difficult because you can never be sure that the example mirrors the real use case, but I think that your scenario might be search-and-criteria, and that your grain should be at the criteria level.
For example, your fact table might look like this:
fact_search (date_id,time_id,search_id,criteria_id,criteria_value)
Thinking about the types of query I might want to do against search data, this design is my best choice. The only issue I see is with the data type of criteria_value, it would have to be a choice/text value, and would definitely be non-additive.
I'm creating a jewellery product catalogue application and I need to store properties for each product such as material, finishes, product type etc.
I've concluded that there needs to be a model for each property, mainly because things like material and finishes might have prices and weights and other things associated with them.
Which of the two options will be the most efficient way to store data and be scalable
Create a model PropertyMap that will map property types and IDs to a Product ID.
Create several other models such as ProductMaterial, ProductFinish etc that will made a property to a product
All the data needs to be searchable & filterable. The database will probably index around 10K products.
Open to other smarter ways to store this data as well!
As a rule of thumb, to get the most out of your database tools, it's best to normalize your data according to the typical SQL conventions. That means that a bunch of fields that have a one-to-one relationship with each other should be collected together into the same table. That way you can grab them all (and they're frequently needed together) with a simple and efficient query.
If you instead have to gather them up from some different organization, both you and the database will end up having to do a lot more work. It will scale poorly, both on the hardware and in your brain as you struggle to maintain and extend it.
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)