Relational data models

What

The relational data model was invented in 1969 at the IBM research department, with the lead researcher Edgar R. Codd. They created the 12 rules (13) to manifest the requirements for a data management system.

Rule 1: The information rule:
All information in a relational data base is represented explicitly at the logical level and in exactly one way – by values in tables.

Why

Relational data models are important as they help us with

standardisation of data models

flexibility in adding and altering tables

data integrity

structured query language can be used

simplicity - the tabular format

intuitive organisation - spreadsheets

When

small data volumes

secondary indices needed

Analytics needed

Aggregations needed

ACID transactions needed

Flexibility for writing queries: In relational data models we are modelling data and not queries.

easy to change, when business requirements change

Structure

Why

free database from unwanted insertions, updates and deletion dependencies

reduce the need of refactoring the database as new types of data are introduced

make the data more informative for users

neutral to quer statistics. We don't want to design for particular queries

Normalisation

Normalisation is needed to reduce data redundancy and to increase data integrity. The overall goal here is to have updates only in one place.

1NF - First Normal Form

Atomic values: each cell contains unique and single values

Be able to add data without altering tables

Separate different relations into different tables

Keep relations between tables together with foreign keys

2NF - Second Normal Form

1NF is reached

All columns in a table rely on the primary key

3NF - Third Normal Form

2NF is reached

No transitive dependencies

There are up to 6 normal forms. But most of the time we will need only up to 3.

Denormalisation

When everything is normalised, we need Joins to gather the data. The more Joins you need, the slower the query will be. Denormalisation must be done to increase performance.

Tables

Fact table

Consists of the measurements, metrics of facts of business process. EVENTS THAT HAVE ACTUALLY HAPPENED - like a transaction

Dimension table

A structure that categorises facts and measures in order to enable users to answer business questions.
Dimensions can be for example people, products, place or time.

Schemas

Star Schema

a physical model resembling a star shape

a fact table is at its center, surrounded by dimension tables

the simplified version of the snowflake scheme

Advantages

denormalised | simplifies queries | fast aggregations

Disadvantages

downsides of denormalisation | data integrity | decrease query flexibility | many to many relationship |

Snowflake Schema

logical arrangement of tables in a multidimensional database represented by centralised fact tables with are connected to multiple dimension.

Advantages

In the 1NF and 2NF it is more normalised than the STAR Schema

Sketchnote

To have all the above information in one view, I made a sketchnote.

If you need a higher resolution please use this page

Useful links