Given below is the schema for the data. There are a total of 12 tables, thus 12 CSV

files, each corresponding to a relational table.

user (email, password, name, date_of_birth, address, type)

primary key(email)

celebrity (email, website, kind)

primary key(email) blurt (blurtid, email, text, location, time)

primary key(blurtid,email)

foreign key(email) references user(email)

hobby (email, hobby)

primary key(email,hobby)

foreign key(email) references user(email))

follow (follower,followee)

primary key(follower,followee)

foreign key(follower) references user(email)

foreign key(followee) references user(email))

vendor (id, name)

primary key(id)

vendor_ambassador (vendorid, email)

primary key(vendorid)

foreign key(email) references user(email)

foreign key(vendorid) references vendor(id))

topic (id, description)

primary key(id)

vendor_topics (vendorid,topicid)

primary key(vendorid, topicid)

foreign key(vendorid) references vendor(id)

foreign key(topicid) references topic(id))

blurt_analysis (email,blurtid,topicid,confidence,sentiment)

primary key(email, blurtid, topicid)

foreign key(email,blurtid) references blurt(email,blurtid)

foreign key(topicid) references topic(id)

constraint confidence >= 0 and confidence <=10

constraint sentiment >= -5 and sentiment <=5

advertisement (id, content, vendorid)primary key(id)

foreign key(vendorid) references vendor(id))

user_ad (email,adid)

primary key(email,adid)

foreign key(email) references user(email)

foreign key(adid) references advertisement(id))

7.Write an SQL query to find all pairs of users (A,B) such that both A and B

have blurted on a common topic but A is not following B. Your query should

print the names of A and B in that order.

Writing in SQL