I am kind of confused with the difference between master data and dimension data. Both of them are said to be relatively stable data, eg, organization information, employee information, producti information， compared with transactional data，such as order.
I would ask what't the difference betweeen master data and dimension data, I think that most dimension table are from master data when doing data analysis?
Thanks
Master data is a classification of the type of data.
Dimensional data is a classification of the way of organising/structuring data
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I'm designing a website where users answer surveys. I need to design a data warehouse to aggregate their responses. So far in my model I have:
A dim table for Users.
A dim table for Questions.
A fact table for UserResponses. <= This is where I'm having the problem.
So the problem I have is that additional comments can be added to their responses. For example, somebody may come in and make 2 comments against a single response. How should I model this in the database?
I was thinking of creating another fact table for "Comments", and linking it to a record in UserResponses. Is this the right thing to do? This additional table would have something like the below columns:
CommentText
Foreign key relationship to fact.UserResponses.
Yes, your idea to create another table is correct. I would typically call it a "child" table rather than calling it another fact table.
The key thing that you didn't mention is that the table comments still needs an ID field. A table without an ID would be bad design (although it is indeed possible to create the table with no ID) since you would have no simple way to refer to individual comments.
In a dimension model, fact tables are never linked to each other, as the grain of the data will be compromised.
The back-end database of a client application is not usually a data warehouse schema, but more of an online transactional processing (OLTP) schema. This is because transactional systems work better with third normal form. Analytical systems work better with dimensional models because the data can be aggregated (i.e., "sliced and diced") more easily.
I would recommend switching back to an OLTP database. It can still be aggregated when needed, but maintains third normal form for easier transactional processing.
Here is a good comparison between a dimensional model (OLAP) and a transactional system (OLTP):
https://www.guru99.com/oltp-vs-olap.html
I am struggling in figuring out how to create a star schema from multiple source tables. I work at a trading firm so the data is related to user trading activity. The issue I am having is that our datasets do not have primary ids for every field that could be a dimension. Instead, we usually relate our data together using the combination of date and account number. Here is an example of 3 source tables...
I would like to turn this into a star schema, something that looks like ...
Is my only option to denormalize my source tables into one wide table (joining trades to position on account number and date, and joining the users table on account number), create keys for each dimension, then re normalizing it into the star schema? Are star schema's ever built from multiple source tables?
Star schemas are almost always created from multiple source tables.
The normal process is:
Populate your dimension tables
Create a temporary/virtual fact record using your source data
Using this fact record, look up the relevant dimension keys
Write the actual fact record to your target fact table
Data-warehousing is about query speed. The data-warehouse should not be concerned with data integrity. IT SHOULD NOT CLEAN OR CORRECT BAD DATA. It only needs to gather all the data together into a single record to present to the model for analysis. Denormalizing the data is how this is done.
In a star schema, dimensions do not know about each other and have no relationships with other dimensions. In a snowflake, dimensions are related to other dimensions. That is the primary difference between star and snowflake.
All the metadata options for events are rolled up into dimensions and used for slicing/filtering. All the measurable/calculation data for an event are in the event fact, along with a reference to the dimension(s) containing the relevant metadata. The Metadata/Dimension is reused across multiple fact records.
Based on the limited example you've provided, I'd suggest you research degenerate dimensions and junk dimensions. Your Trade and Position data may need to be turned into a fact and a dimension (degenerate), and some of your flag attributes may be best placed into a junk dimension.
You should also make sure your dimension keys are clear. You should not have multiple paths to a dimension (accountnumber: trade -> position -> user & trade -> user ) as that will cause inconsistent results when querying depending on which relationship you traverse.
Going to do a POC on Snowflake and just wanted to check what is the best practice around loading the data to snowflake:
Should load data in normalized (Group and store related information into multiple tables) Or go with Denormalized form? What is recommended here..?
Or dump data to one table and create multiple views from one table? But think that The big table has 150 million records and it has a column called Australia State and we know that we have only 6 states in Australia. If a create a view to extract Australia State information from main table via view, I feel like it will be more costly than store Australia State Information in a separate table and that is what I am talking about normalization..?
What is the way to load SCD-2 dimensions in Snowflake? Interested to know the efficient way to do this..?
Your questions 1. and 2. seem to be more about partitioning (or "clustering" in Snowflake lingo) than normalization. It is also about performance vs. maintainability.
The best of two worlds would be to have a single table where Australia State is a clustering key. Correct setup will allow for efficient Query pruning. Read more in Clustering Keys & Clustered Tables.
Re. question 3. Look into MERGE. Maybe you also can get some hints reading Working with SCD-Type-II in Snowflake
I would load the data the way that "makes the most sense for how it will be 'updated' and 'used'"
Which is to mean we have data (many forms actually) that we sync/stream from PostgreSQL DB's, and some of it we dimension it (SCD1/SCD2/SCD6) as we load it. For this data we have the update timestamp we we load the record, we workout the changes and build the dimension data.
If you already have dimension data, and it's a single data move. Dump the tables you have and just load them. It's really cheap to make a new table in snowflake, so we just tried stuff and worked out what fitted our data ingress patterns, and how we were reading the data to improve/help clustering, or avoid churn that costs on the auto-clustering operations.
I recently build a simple data warehouse with 2 dimension tables and 1 fact table.
First Dim hold the user input "queryId, dna sequence, dna database name, other parameters".
Second Dim hold database description "databaseId, other parameters".
The fact table will hold the result of the search "queryId, databaseID, hit founded, other parameters describe the hit".
Now, where should I upload the data (The result)? To the fact table? or to the dimensions table?
To where should I upload "queryId and databaseID"? because they are in dimensions and in the fact. Sorry for this question but, I am new to DW.
Thanks a lot,
You have to create an ETL that loads like this (this assumes we rebuild the DW on each import, the steps are different for incremental loading):
Truncate fact table
Truncate dimensions
Populate dimensions, (your keys should be in the dimensions)
Populate the fact with with your dimension keys and measures
Then, when querying, you'll join your dimensions onto your fact via the keys.
Neither nor.
You UPLOAD data into staging tables. Those are created for optimal upload speed. Staging tables may be flat, may not be complete and require joining with other tables.
Then you use a loading process to load them from staging into the data warehouse.
I'm wanting to use OLAP cubes and have to first design a data warehouse. I am going for the star-schema. I'm a little confused about how to convert from a normal database to a data warehouse, especially with regards to foreign keys between dimension tables. I know a fact table has foreign keys to dimensions, but do dimensions have foreign keys between them? For example, what do I need to do with the following 2 examples:
TABLE: Airports
COLUMNS: Id, Name, Code, CityId
When I make the Airports dimension, do I remove CityId and put the City Name instead? Or what?
TABLE: Regions
COLUMNS: Id, Name, RegionType, ParentId
The question for this one is mostly the same, but a bit more complex, because here ParentId refers to the same table (Regions).. example: a City can refer to a parent Country record. How do I translate these over to a data warehouse star schema?
Lastly, regarding measures, those go on the fact table, right? I think I will likely need multiple fact tables. Is that normal? Does one fact table translate to one OLAP cube? Or what?
You want to include city within your airport dimension. You are intentionally flattening out your normalised schema to aid the speed of the dimensional model which can seem counter intuitive if you are coming from transactional development.
With regards to the perennial child relationship, you want the parented to be translated into the surrogate of the region record. Ssas will provide the functionality to relate parent child records when you are designing your cube.
Multiple facts are not unusual, but unless the fact data is completely unrelated, there is no need to separate them into different cubes. The requirement for multiple facts will be driven by having data at a different grain. Keep all of you metrics (I.e. Flights) together, but you would separate out flight metrics from food sale metrics
you not converting to data warehouse, you are creating new data warehouse with few dimension and 1 (at least) Fact table. dimension tables are loaded first and you DO NOT want to change id with name.
you need additional key for each dimension table. once you load dimensions, I usually use ssis package to load fact table.(either incremental load or you can truncate fact table each time before you load with new data( depends what you need) ...