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Data Integration (Joins (2 Types: (Outer Join (Left outer join (Produces…
Data Integration
Joins
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2 Types:
Inner Join
Each row in the left table is combined horizontally with any row
in the right table that has the same identity value
Outer Join
Left outer join
Produces the same result as an inner join but also adds any rows from the left table that do not have corresponding rows in the right table
Right outer join
Produces the same result as an inner join, but also adds any rows from the right table that do not have corresponding rows in the left table
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If we are engaged in a project with the
goal being to collect as much customer data as possible, then the left outer join is most useful
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Unions
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Generally, we perform unions when we have datasets that contain
unique sets of cases sharing the same or very similar columns
If there is no overlap
but simply additional data points a union is a more appropriate tool for the
combining of lists that consist of different people
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In addition to obtaining more observations, we can also look for additional features of interest that we do not currently have, at which point it will invariably be necessary to integrate data from different sources