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 Doing well  Deadline moved to Friday  Easy meter

 Name, major  Learning objectives  Things you like about the class  Things that can be improved  Attitude towards the Tournament

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Primary key: a unique identifier used to retrieve the record One Many manages has

manages has Bruce Wayne, Gotham, NY

Insert into Customer (c_id, f_name, l_name, city, state) values (7759, 'Bruce', 'Wayne', 'Gotham', 'NY')

manages has Bruce Wayne, Gotham, NY C_id = 7759

Insert into insurance_plan (c_id, coverage, premimum) values (7759, , 500)

manages has Bruce Wayne, Cville, VA

Update customer set city = ‘Charlottesville', state = ‘VA' where c_id = 7759

manages has Bruce Wayne, C_id 7759 L_id = 1070 $10,000,000 4% Due Dec 31 Barbara Goodhue Lo_id 16

Insert into loan (l_id, principal, rate, date_due, lo_id) Values (1070, , '12/31/2016', 16) Insert into customer_in_loan (c_id, l_id) values (7759, 1070)

 Directly related to business operations  Single customer, single contract, deal, service…  Often INSERTs  Small amount of data  Large numbers of fast, simple queries

manages has

select customer.state, sum(coverage) from customer, insurance_plan where customer.c_id = insurance_plan.c_id group by customer.state

manages has

select top 3 customer.c_id, customer.l_name, sum(loan.principal) from customer, customer_in_loan, loan where customer.c_id = customer_in_loan.c_id and customer_in_loan.l_id = loan.l_id group by customer.c_id, customer.l_name order by sum(loan.principal) desc

manages has

select customer.city, avg(loan.rate) from customer, customer_in_loan, loan where customer.c_id = customer_in_loan.c_id and customer_in_loan.l_id = loan.l_id group by customer.city order by avg(loan.rate) desc

 Relate to decision making more than business operations  Aggregate customers, contracts, deals, services…  Mostly Selects, often Joins  Larger amount of data  Small number of larger, complex queries

manages has

n/a

 Questions relate to decision making, more than business operations  SQL provides the input data, but is not sufficient  Analytics require additional software (SPSS, SAS, R, Data miner…)  More similar to BI queries than operational queries.

Transactions / Operations Real time, individual, action Business intelligence Analytics Historical, aggregate, decision Orders Customers Product s Data Warehouse Extract Clean Transform Load Query Report Analyze Visualize Technical consultants Data scientists Business consultants Data scientists Managers & Decision makers

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