I have a raw table having 28 columns and around 300 rows that I want to normalize. I wonder whether I should divide the table into sub tables based on entity characteristics in advance before normalization.
Also, should I figure out relationship (one-to-one, one-to-many, many-to-many) between each sub table before normalization? As far as I know, I have to make something called junction table for many-to-many relationship. I am confused about the procedure of designing a database given a single raw table. I appreciate your comments. Thank you.
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I want to store Genetic variants of patients in a database. Number of patients are around 1000 but for each patient there are more than 100,000 Genetic variants. Can anyone advice me how to design this table. Is it possible to do this in MongoDB ? The data will look something like this. Any help will be highly appreciated
If you want to use a relational database. You should create a table for just patients and a separate table that has a foreign key pointing to the user and another column for the genetic variation. Instead of having a column per RS_***, those should be a row. So for example User A will have 100,000 rows in Table B each row being RS_{insert_index}.
I'm new to the data world and trying to get started. I have imported 2 tables to power BI. They have common columns but none of the rows are unique. Even when merging up to 7 columns a many-many connection is still created. I am trying to get a 1-1 or 1-many join, as I have been told it's best practice. I'm at a bit of a loss on what to do.
So if I'm understanding the logic correctly, no unique rows means no PK, and only many-many relationships can be created.
I've tried to create unique rows by merging up-to 7 columns, but still some of the rows aren't unique.
Any other work around? I appreciate all help
You need to create a "Dimension" table in between. Pick one common column, extract it from the table and filter for unique/disinct values. (You could also combine this column from both tables in case they don't have all the same values. This is the one side of the relationship. Now you connect to the common columns of both "Fact" tables, which become the many side of the relationship. This allows you to filter from Dim to both Facts. Avoid using bidirectional fitering.
I'm building a simple star schema in data warehouse with two dimensions based off of business entities: dim_loan and dim_borrower. There are also some fact tables, such as fact_loan_status which has one row per month for each loan showing the balance at that time, and has an FK back to dim_loan.
So here's my question: if dim_loan has a FK for borrower_id back to dim_borrower, does that violate star schema? Nearly all discussion of the star schema revolves around individual dim tables that only have FK relations with fact tables, not fellow dims. Making a fact_loan_borrower doesn't make sense to me for this simple one-to-one relationship.
Any advice would be welcomed!
if dim_borrower and dim_loan have the same cardinality, then keeping both ids (loan_id, borrower_id) in the fact_loan_borrower would help you gain performance. You need only one join to bring borrower or loan information from respective dimensions. If you keep borrower_id as FK in dim_loan you need to use two joins if you need to bring borrowers information.
If the two dimensions have different cardinality then it is wise to attach dimension with low cardinality with the fact table - it will help to keep fact table small.
The choice of star and snowflake schema fully depends on you.
i am having a hard time creating an ERD for my table relationship. I have 4 tables: film, ticket_type, studio, and schedule. The schedule table is a relationship table that contain the primary key from the other three tables as foreign key. The question is how can i picture it in ERD? Something like many-to-many relationship but with 3 table, is it possible to do it like this? The database works fine when i try to create it so i think there's no problem in my concept. Thanks in advance.
Edit: forgot to add the ticket_type table is for pricing and type like: 2d,3d,or 4d, i create it like this to avoid redundancy.
One more question, can i add another field to a relationship table? If I remember correctly it should be fine, but just to make sure.
If schedule is a relationship, it would be represented as follows on an entity-relationship diagram:
Relationships are identified by the keys of the related entities. A table diagram makes this more visible:
However, if schedule is an entity set with relationships to the other 3 entity sets, it would be represented as follows on an ER diagram:
If we map every entity set and relationship to its own table, we get the following table diagram:
However, if we denormalize the relationship tables into the schedule table (since they all have the same primary key), our table diagram changes to:
Compare this with the first table diagram. While these physical models are very similar, they derive from very different conceptual models. Strictly speaking, I think both "entity table" and "relationship table" are inappropriate for the denormalized schedule table. In the network data model, we would call it an associative entity (but that's not the same as associative entities in the ER model).
Finally, relationships can have attributes too:
I've read a lot of tips and tutorials about normalization but I still find it hard to understand how and when we need normalization. So right now I need to know if this database design for an electricity monitoring system needs to be normalized or not.
So far I have one table with fields:
monitor_id
appliance_name
brand
ampere
uptime
power_kWh
price_kWh
status (ON/OFF)
This monitoring system monitors multiple appliances (TV, Fridge, washing machine) separately.
So does it need to be normalized further? If so, how?
Honestly, you can get away without normalizing every database. Normalization is good if the database is going to be a project that affects many people or if there are performance issues and the database does OLTP. Database normalization in many ways boils down to having larger numbers of tables themselves with fewer columns. Denormalization involves having fewer tables with larger numbers of columns.
I've never seen a real database with only one table, but that's ok. Some people denormalize their database for reporting purposes. So it isn't always necessary to normalize a database.
How do you normalize it? You need to have a primary key (on a column that is unique or a combination of two or more columns that are unique in their combined form). You would need to create another table and have a foreign key relationship. A foreign key relationship is a pair of columns that exist in two or more tables. These columns need to share the same data type. These act as a map from one table to another. The tables are usually separated by real-world purpose.
For example, you could have a table with status, uptime and monitor_id. This would have a foreign key relationship to the monitor_id between the two tables. Your original table could then drop the uptime and status columns. You could have a third table with Brands, Models and the things that all models have in common (e.g., power_kWh, ampere, etc.). There could be a foreign key relationship to the first table based on model. Then the brand column could be eliminated (via the DDL command DROP) from the first table as this third table will have it relating from the model name.
To create new tables, you'll need to invoke a DDL command CREATE TABLE newTable with a foreign key on the column that will in effect be shared by the new table and the original table. With foreign key constraints, the new tables will share a column. The tables will have less information in them (fewer columns) when they are highly normalized. But there will be more tables to accommodate and store all the data. This way you can update one table and not put a lock on all the other columns in a denormalized database with one big table.
Once new tables have the data in the column or columns from the original table, you can drop those columns from the original table (except for the foreign key column). To drop columns, you need to invoke DDL commands (ALTER TABLE originalTable, drop brand).
In many ways, performance will be improved if you try to do many reads and writes (commit many transactions) on a database table in a normalized database. If you use the table as a report, and want to present all the data as it is in the table normally, normalized the database will hurt the peformance.
By the way, normalizing the database can prevent redundant data. This can make the database consume less storage space and use less memory.
It is nice to have our database normalize.It helps us to have a efficient data because we can prevent redundancy here and also saves memory usages. On normalizing tables we need to have a primary key in each table and use this to connect to another table and when the primary key (unique in each table) is on another table it is called the foreign key (use to connect to another table).
Sample you already have this table :
Table name : appliances_tbl
-inside here you have
-appliance_id : as the primary key
-appliance_name
-brand
-model
and so on about this appliances...
Next you have another table :
Table name : appliance_info_tbl (anything for a table name and must be related to its fields)
-appliance_info_id : primary key
-appliance_price
-appliance_uptime
-appliance_description
-appliance_id : foreign key (so you can get the name of the appliance by using only its id)
and so on....
You can add more table like that but just make sure that you have a primary key in each table. You can also put the cardinality to make your normalizing more understandable.