Consider a database that contains data about a music festival that you are organising. Now, while designing the database, you may have tables pertaining to the following:
- Data about the artists who would be performing at the festival.
- Details of the stages on which the artists would perform.
- Timings for each performance on each stage.
- Details of organisers and other details of security guards, volunteers, photographers, etc.
- Details of attendees, including name, age, address, ticket category, etc.
As you can see, the design of such a database can become quite complicated. How would you analyse the data of the artists and attendees together? This information may be required to calculate the feasibility of the event in terms of cost. This is where joins can be used to combine multiple tables and make the analysis of such data easier for you.
Let’s understand the joins in our market example in the next two video.
Let’s understand more about joins in the next video.
As you learnt in the two videos given above, there are mainly two types of joins: inner joins and outer joins. While an inner join searches tables for matching or overlapping data, an outer join also returns rows for which there is no match between the tables.
In the next segment, you will see the demonstration of different types of joins.