What are the join types we have in SQL and DataStage ?

Both SQL and DataStage support various types of joins to combine data from different tables or datasets. Here's a comparison:

SQL Join Types

1. Inner Join: Returns only the rows where there is a match in both tables.

2. Left (Outer) Join: Returns all rows from the left table and the matched rows from the right table. Unmatched rows from the left table will have NULLs for columns from the right table.

3. Right (Outer) Join: Returns all rows from the right table and the matched rows from the left table. Unmatched rows from the right table will have NULLs for columns from the left table.

4. Full (Outer) Join: Returns rows when there is a match in one of the tables. This means it returns all rows from both the left and right tables, with NULLs in places where there is no match.

5. Cross Join: Returns the Cartesian product of the two tables, meaning every row from the first table is paired with every row from the second table.

6. Self Join: Joins a table with itself to compare rows within the same table.

DataStage Join Types

1. Inner Join: Similar to SQL, returns rows where there is a match in both datasets.

2. Left (Outer) Join: Returns all rows from the left dataset and the matched rows from the right dataset. Unmatched rows from the left dataset will have NULLs for columns from the right dataset.

3. Right (Outer) Join: Returns all rows from the right dataset and the matched rows from the left dataset. Unmatched rows from the right dataset will have NULLs for columns from the left dataset.

4. Full (Outer) Join: Returns all rows from both datasets, with NULLs in places where there is no match.

5. Cross Join: Produces the Cartesian product of the datasets, meaning each row from the first dataset is paired with each row from the second dataset.

Here's a summary in a table format:

Join Type	SQL	DataStage
Inner Join	Matches in both tables	Matches in both datasets
Left (Outer) Join	All rows from the left table, matched from the right	All rows from the left dataset, matched from the right
Right (Outer) Join	All rows from the right table, matched from the left	All rows from the right dataset, matched from the left
Full (Outer) Join	All rows from both tables	All rows from both datasets
Cross Join	Cartesian product of both tables	Cartesian product of both datasets
Self Join	Joins the table with itself	Not specifically a stage, but can be implemented by joining a dataset with itself

These join types help you effectively combine and analyze data from different sources in both SQL and DataStage.

Related Hashtags:

#datastage #DataStage #Datastage #SQL #sql #dwh #DWH #Dwh

#datawarehouse #datawarehousing #unix #ibmds #ibmdatastage

#BMDatastage #IBMDataStage #interviewquestions #interviewtips 

#interview #interviewprep #interviewskills #interviewpreparation 

#jobinterview #interviews #interviewready #interviewing #jobsearch 

#jobseekers #careeradvice #resumetips #jobinterviewtips #career 

#interviewadvice #jobs #interviewcoaching #job #careerdevelopment 

#interviewoutfit #careertips #interviewcoach #jobsearching #interviewer 

#careercoach #recruitment #resume #hiring

#blog #blogger #fashion #love #instagram

#instagood #photography #lifestyle #travel #blogging #blogpost

#bloggerstyle #follow #style #like #food #bloggers #fashionblogger

#music #art #photooftheday #beauty #bloggerlife #life #influencer 

#k #instadaily #instablog #foodblogger #vlog

#jobinterviews #motivation #jobsearchtips #jobvacancy 

#jobinterviewquestions #jobseeker #resumewriting #jobhunt 

#resumewriter #resumehelp #coding #careergoals #jobhunting 

#knowledge #goals #facts #interviewhelp #jobsinindia #jobseeking 

#interviewseason #recruiting #quotes #questions #instafacts #factsdaily 

#unknownfacts #dailyfacts #hiringnow #factoflife #allfacts