1.创建自然连接，NATURAL JOIN子句，会以两个表中具有相同名字的列为条件创建等值连接。
SQL> select department_id,department_name,location_id,city
2 from departments natural join locations;
DEPARTMENT_ID DEPARTMENT_NAME LOCATION_ID
------------- ------------------------------ -----------
CITY
------------------------------
60 IT 1400
Southlake
50 Shipping 1500
South San Francisco
10 Administration 1700
Seattle
DEPARTMENT_ID DEPARTMENT_NAME LOCATION_ID
------------- ------------------------------ -----------
CITY
------------------------------
30 Purchasing 1700
Seattle
90 Executive 1700
Seattle
100 Finance 1700
Seattle
DEPARTMENT_ID DEPARTMENT_NAME LOCATION_ID
------------- ------------------------------ -----------
CITY
------------------------------
110 Accounting 1700
Seattle
120 Treasury 1700
Seattle
130 Corporate Tax 1700
Seattle
DEPARTMENT_ID DEPARTMENT_NAME LOCATION_ID
------------- ------------------------------ -----------
CITY
------------------------------
140 Control And Credit 1700
Seattle
150 Shareholder Services 1700
Seattle
160 Benefits 1700
Seattle
DEPARTMENT_ID DEPARTMENT_NAME LOCATION_ID
------------- ------------------------------ -----------
CITY
------------------------------
170 Manufacturing 1700
Seattle
180 Construction 1700
Seattle
190 Contracting 1700
Seattle
DEPARTMENT_ID DEPARTMENT_NAME LOCATION_ID
------------- ------------------------------ -----------
CITY
------------------------------
200 Operations 1700
Seattle
210 IT Support 1700
Seattle
220 NOC 1700
Seattle
DEPARTMENT_ID DEPARTMENT_NAME LOCATION_ID
------------- ------------------------------ -----------
CITY
------------------------------
230 IT Helpdesk 1700
Seattle
240 Government Sales 1700
Seattle
250 Retail Sales 1700
Seattle
DEPARTMENT_ID DEPARTMENT_NAME LOCATION_ID
------------- ------------------------------ -----------
CITY
------------------------------
260 Recruiting 1700
Seattle
270 Payroll 1700
Seattle
20 Marketing 1800
Toronto
DEPARTMENT_ID DEPARTMENT_NAME LOCATION_ID
------------- ------------------------------ -----------
CITY
------------------------------
40 Human Resources 2400
London
80 Sales 2500
Oxford
70 Public Relations 2700
Munich
27 rows selected.
2.使用 USING 子句创建连接，如果多个列具有相同的名称，但自然连接的数据类型又不匹配，则可以使用using子句来指定，使用一个等值的列。
SQL> select employee_id,last_name,location_id,department_id
2 from employees join departments using (department_id);
EMPLOYEE_ID LAST_NAME LOCATION_ID DEPARTMENT_ID
----------- ------------------------- ----------- -------------
200 Whalen 1700 10
201 Hartstein 1800 20
202 Fay 1800 20
114 Raphaely 1700 30
119 Colmenares 1700 30
115 Khoo 1700 30
116 Baida 1700 30
117 Tobias 1700 30
118 Himuro 1700 30
203 Mavris 2400 40
198 OConnell 1500 50
EMPLOYEE_ID LAST_NAME LOCATION_ID DEPARTMENT_ID
----------- ------------------------- ----------- -------------
199 Grant 1500 50
120 Weiss 1500 50
121 Fripp 1500 50
122 Kaufling 1500 50
123 Vollman 1500 50
124 Mourgos 1500 50
125 Nayer 1500 50
126 Mikkilineni 1500 50
127 Landry 1500 50
128 Markle 1500 50
129 Bissot 1500 50
EMPLOYEE_ID LAST_NAME LOCATION_ID DEPARTMENT_ID
----------- ------------------------- ----------- -------------
130 Atkinson 1500 50
131 Marlow 1500 50
132 Olson 1500 50
133 Mallin 1500 50
134 Rogers 1500 50
135 Gee 1500 50
136 Philtanker 1500 50
137 Ladwig 1500 50
138 Stiles 1500 50
139 Seo 1500 50
140 Patel 1500 50
EMPLOYEE_ID LAST_NAME LOCATION_ID DEPARTMENT_ID
----------- ------------------------- ----------- -------------
141 Rajs 1500 50
142 Davies 1500 50
143 Matos 1500 50
144 Vargas 1500 50
180 Taylor 1500 50
181 Fleaur 1500 50
182 Sullivan 1500 50
183 Geoni 1500 50
184 Sarchand 1500 50
185 Bull 1500 50
186 Dellinger 1500 50
EMPLOYEE_ID LAST_NAME LOCATION_ID DEPARTMENT_ID
----------- ------------------------- ----------- -------------
187 Cabrio 1500 50
188 Chung 1500 50
189 Dilly 1500 50
190 Gates 1500 50
191 Perkins 1500 50
192 Bell 1500 50
193 Everett 1500 50
194 McCain 1500 50
195 Jones 1500 50
196 Walsh 1500 50
197 Feeney 1500 50
EMPLOYEE_ID LAST_NAME LOCATION_ID DEPARTMENT_ID
----------- ------------------------- ----------- -------------
104 Ernst 1400 60
103 Hunold 1400 60
107 Lorentz 1400 60
106 Pataballa 1400 60
105 Austin 1400 60
204 Baer 2700 70
176 Taylor 2500 80
177 Livingston 2500 80
179 Johnson 2500 80
175 Hutton 2500 80
174 Abel 2500 80
EMPLOYEE_ID LAST_NAME LOCATION_ID DEPARTMENT_ID
----------- ------------------------- ----------- -------------
173 Kumar 2500 80
172 Bates 2500 80
171 Smith 2500 80
170 Fox 2500 80
169 Bloom 2500 80
168 Ozer 2500 80
145 Russell 2500 80
146 Partners 2500 80
147 Errazuriz 2500 80
148 Cambrault 2500 80
149 Zlotkey 2500 80
EMPLOYEE_ID LAST_NAME LOCATION_ID DEPARTMENT_ID
----------- ------------------------- ----------- -------------
150 Tucker 2500 80
151 Bernstein 2500 80
152 Hall 2500 80
153 Olsen 2500 80
154 Cambrault 2500 80
155 Tuvault 2500 80
156 King 2500 80
157 Sully 2500 80
158 McEwen 2500 80
159 Smith 2500 80
160 Doran 2500 80
EMPLOYEE_ID LAST_NAME LOCATION_ID DEPARTMENT_ID
----------- ------------------------- ----------- -------------
161 Sewall 2500 80
162 Vishney 2500 80
163 Greene 2500 80
164 Marvins 2500 80
165 Lee 2500 80
166 Ande 2500 80
167 Banda 2500 80
101 Kochhar 1700 90
100 King 1700 90
102 De Haan 1700 90
110 Chen 1700 100
EMPLOYEE_ID LAST_NAME LOCATION_ID DEPARTMENT_ID
----------- ------------------------- ----------- -------------
108 Greenberg 1700 100
111 Sciarra 1700 100
112 Urman 1700 100
113 Popp 1700 100
109 Faviet 1700 100
206 Gietz 1700 110
205 Higgins 1700 110
106 rows selected.
3.自然连接中是以具有相同名字的列为连接条件的，使用ON子句指定要连接任意条件或指定列连接条件。
SQL> select e.employee_id,e.last_name,e.department_id,d.department_id,d.location_id
2 from employees e join departments d on (e.department_id = d.department_id);
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
200 Whalen 10 10 1700
201 Hartstein 20 20 1800
202 Fay 20 20 1800
114 Raphaely 30 30 1700
119 Colmenares 30 30 1700
115 Khoo 30 30 1700
116 Baida 30 30 1700
117 Tobias 30 30 1700
118 Himuro 30 30 1700
203 Mavris 40 40 2400
198 OConnell 50 50 1500
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
199 Grant 50 50 1500
120 Weiss 50 50 1500
121 Fripp 50 50 1500
122 Kaufling 50 50 1500
123 Vollman 50 50 1500
124 Mourgos 50 50 1500
125 Nayer 50 50 1500
126 Mikkilineni 50 50 1500
127 Landry 50 50 1500
128 Markle 50 50 1500
129 Bissot 50 50 1500
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
130 Atkinson 50 50 1500
131 Marlow 50 50 1500
132 Olson 50 50 1500
133 Mallin 50 50 1500
134 Rogers 50 50 1500
135 Gee 50 50 1500
136 Philtanker 50 50 1500
137 Ladwig 50 50 1500
138 Stiles 50 50 1500
139 Seo 50 50 1500
140 Patel 50 50 1500
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
141 Rajs 50 50 1500
142 Davies 50 50 1500
143 Matos 50 50 1500
144 Vargas 50 50 1500
180 Taylor 50 50 1500
181 Fleaur 50 50 1500
182 Sullivan 50 50 1500
183 Geoni 50 50 1500
184 Sarchand 50 50 1500
185 Bull 50 50 1500
186 Dellinger 50 50 1500
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
187 Cabrio 50 50 1500
188 Chung 50 50 1500
189 Dilly 50 50 1500
190 Gates 50 50 1500
191 Perkins 50 50 1500
192 Bell 50 50 1500
193 Everett 50 50 1500
194 McCain 50 50 1500
195 Jones 50 50 1500
196 Walsh 50 50 1500
197 Feeney 50 50 1500
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
104 Ernst 60 60 1400
103 Hunold 60 60 1400
107 Lorentz 60 60 1400
106 Pataballa 60 60 1400
105 Austin 60 60 1400
204 Baer 70 70 2700
176 Taylor 80 80 2500
177 Livingston 80 80 2500
179 Johnson 80 80 2500
175 Hutton 80 80 2500
174 Abel 80 80 2500
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
173 Kumar 80 80 2500
172 Bates 80 80 2500
171 Smith 80 80 2500
170 Fox 80 80 2500
169 Bloom 80 80 2500
168 Ozer 80 80 2500
145 Russell 80 80 2500
146 Partners 80 80 2500
147 Errazuriz 80 80 2500
148 Cambrault 80 80 2500
149 Zlotkey 80 80 2500
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
150 Tucker 80 80 2500
151 Bernstein 80 80 2500
152 Hall 80 80 2500
153 Olsen 80 80 2500
154 Cambrault 80 80 2500
155 Tuvault 80 80 2500
156 King 80 80 2500
157 Sully 80 80 2500
158 McEwen 80 80 2500
159 Smith 80 80 2500
160 Doran 80 80 2500
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
161 Sewall 80 80 2500
162 Vishney 80 80 2500
163 Greene 80 80 2500
164 Marvins 80 80 2500
165 Lee 80 80 2500
166 Ande 80 80 2500
167 Banda 80 80 2500
101 Kochhar 90 90 1700
100 King 90 90 1700
102 De Haan 90 90 1700
110 Chen 100 100 1700
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
108 Greenberg 100 100 1700
111 Sciarra 100 100 1700
112 Urman 100 100 1700
113 Popp 100 100 1700
109 Faviet 100 100 1700
206 Gietz 110 110 1700
205 Higgins 110 110 1700
106 rows selected.
4.使用ON 子句创建多表连接
SQL> select employee_id,city,department_name
2 from employees e join departments d on d.department_id = e.department_id
3 join locations l on d.location_id = l.location_id;
EMPLOYEE_ID CITY DEPARTMENT_NAME
----------- ------------------------------ ------------------------------
100 Seattle Executive
101 Seattle Executive
102 Seattle Executive
103 Southlake IT
104 Southlake IT
105 Southlake IT
106 Southlake IT
107 Southlake IT
108 Seattle Finance
109 Seattle Finance
110 Seattle Finance
EMPLOYEE_ID CITY DEPARTMENT_NAME
----------- ------------------------------ ------------------------------
111 Seattle Finance
112 Seattle Finance
113 Seattle Finance
114 Seattle Purchasing
115 Seattle Purchasing
116 Seattle Purchasing
117 Seattle Purchasing
118 Seattle Purchasing
119 Seattle Purchasing
120 South San Francisco Shipping
121 South San Francisco Shipping
EMPLOYEE_ID CITY DEPARTMENT_NAME
----------- ------------------------------ ------------------------------
122 South San Francisco Shipping
123 South San Francisco Shipping
124 South San Francisco Shipping
125 South San Francisco Shipping
126 South San Francisco Shipping
127 South San Francisco Shipping
128 South San Francisco Shipping
129 South San Francisco Shipping
130 South San Francisco Shipping
131 South San Francisco Shipping
132 South San Francisco Shipping
EMPLOYEE_ID CITY DEPARTMENT_NAME
----------- ------------------------------ ------------------------------
133 South San Francisco Shipping
134 South San Francisco Shipping
135 South San Francisco Shipping
136 South San Francisco Shipping
137 South San Francisco Shipping
138 South San Francisco Shipping
139 South San Francisco Shipping
140 South San Francisco Shipping
141 South San Francisco Shipping
142 South San Francisco Shipping
143 South San Francisco Shipping
EMPLOYEE_ID CITY DEPARTMENT_NAME
----------- ------------------------------ ------------------------------
144 South San Francisco Shipping
145 Oxford Sales
146 Oxford Sales
147 Oxford Sales
148 Oxford Sales
149 Oxford Sales
150 Oxford Sales
151 Oxford Sales
152 Oxford Sales
153 Oxford Sales
154 Oxford Sales
EMPLOYEE_ID CITY DEPARTMENT_NAME
----------- ------------------------------ ------------------------------
155 Oxford Sales
156 Oxford Sales
157 Oxford Sales
158 Oxford Sales
159 Oxford Sales
160 Oxford Sales
161 Oxford Sales
162 Oxford Sales
163 Oxford Sales
164 Oxford Sales
165 Oxford Sales
EMPLOYEE_ID CITY DEPARTMENT_NAME
----------- ------------------------------ ------------------------------
166 Oxford Sales
167 Oxford Sales
168 Oxford Sales
169 Oxford Sales
170 Oxford Sales
171 Oxford Sales
172 Oxford Sales
173 Oxford Sales
174 Oxford Sales
175 Oxford Sales
176 Oxford Sales
EMPLOYEE_ID CITY DEPARTMENT_NAME
----------- ------------------------------ ------------------------------
177 Oxford Sales
179 Oxford Sales
180 South San Francisco Shipping
181 South San Francisco Shipping
182 South San Francisco Shipping
183 South San Francisco Shipping
184 South San Francisco Shipping
185 South San Francisco Shipping
186 South San Francisco Shipping
187 South San Francisco Shipping
188 South San Francisco Shipping
EMPLOYEE_ID CITY DEPARTMENT_NAME
----------- ------------------------------ ------------------------------
189 South San Francisco Shipping
190 South San Francisco Shipping
191 South San Francisco Shipping
192 South San Francisco Shipping
193 South San Francisco Shipping
194 South San Francisco Shipping
195 South San Francisco Shipping
196 South San Francisco Shipping
197 South San Francisco Shipping
198 South San Francisco Shipping
199 South San Francisco Shipping
EMPLOYEE_ID CITY DEPARTMENT_NAME
----------- ------------------------------ ------------------------------
200 Seattle Administration
201 Toronto Marketing
202 Toronto Marketing
203 London Human Resources
204 Munich Public Relations
205 Seattle Accounting
206 Seattle Accounting
106 rows selected.
5.使用AND子句或WHERE子句适用附加条件：
①SQL> SELECT e.employee_id, e.last_name, e.department_id,
d.department_id, d.l 2 ocation_id
FROM employees e JOIN departments d
ON (e.department_id = d.department_id)
AND e.manager_id = 149 ;
3 4 5
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
174 Abel 80 80 2500
175 Hutton 80 80 2500
179 Johnson 80 80 2500
177 Livingston 80 80 2500
176 Taylor 80 80 2500
②SQL> SELECT e.employee_id, e.last_name, e.department_id,
d.department_id, d.location_id
FROM employees e JOIN departments d
ON (e.department_id = d.dep 2 3 4 artment_id)
WHERE e.manager_id = 149 ;
5
EMPLOYEE_ID LAST_NAME DEPARTMENT_ID DEPARTMENT_ID LOCATION_ID
----------- ------------------------- ------------- ------------- -----------
174 Abel 80 80 2500
175 Hutton 80 80 2500
179 Johnson 80 80 2500
177 Livingston 80 80 2500
176 Taylor 80 80 2500
6.左右连接：
①SQL> SELECT e.last_name, e.department_id, d.department_name
FROM employees e LEFT O 2 UTER JOIN departments d
ON (e.department_id = d.department_id) ;
3
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Whalen 10 Administration
Fay 20 Marketing
Hartstein 20 Marketing
Colmenares 30 Purchasing
Himuro 30 Purchasing
Tobias 30 Purchasing
Baida 30 Purchasing
Khoo 30 Purchasing
Raphaely 30 Purchasing
Mavris 40 Human Resources
Feeney 50 Shipping
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Walsh 50 Shipping
Jones 50 Shipping
McCain 50 Shipping
Everett 50 Shipping
Bell 50 Shipping
Perkins 50 Shipping
Gates 50 Shipping
Dilly 50 Shipping
Chung 50 Shipping
Cabrio 50 Shipping
Dellinger 50 Shipping
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Bull 50 Shipping
Sarchand 50 Shipping
Geoni 50 Shipping
Sullivan 50 Shipping
Fleaur 50 Shipping
Taylor 50 Shipping
Vargas 50 Shipping
Matos 50 Shipping
Davies 50 Shipping
Rajs 50 Shipping
Patel 50 Shipping
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Seo 50 Shipping
Stiles 50 Shipping
Ladwig 50 Shipping
Philtanker 50 Shipping
Gee 50 Shipping
Rogers 50 Shipping
Mallin 50 Shipping
Olson 50 Shipping
Marlow 50 Shipping
Atkinson 50 Shipping
Bissot 50 Shipping
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Markle 50 Shipping
Landry 50 Shipping
Mikkilineni 50 Shipping
Nayer 50 Shipping
Mourgos 50 Shipping
Vollman 50 Shipping
Kaufling 50 Shipping
Fripp 50 Shipping
Weiss 50 Shipping
Grant 50 Shipping
OConnell 50 Shipping
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Lorentz 60 IT
Pataballa 60 IT
Austin 60 IT
Ernst 60 IT
Hunold 60 IT
Baer 70 Public Relations
Johnson 80 Sales
Livingston 80 Sales
Taylor 80 Sales
Hutton 80 Sales
Abel 80 Sales
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Kumar 80 Sales
Bates 80 Sales
Smith 80 Sales
Fox 80 Sales
Bloom 80 Sales
Ozer 80 Sales
Banda 80 Sales
Ande 80 Sales
Lee 80 Sales
Marvins 80 Sales
Greene 80 Sales
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Vishney 80 Sales
Sewall 80 Sales
Doran 80 Sales
Smith 80 Sales
McEwen 80 Sales
Sully 80 Sales
King 80 Sales
Tuvault 80 Sales
Cambrault 80 Sales
Olsen 80 Sales
Hall 80 Sales
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Bernstein 80 Sales
Tucker 80 Sales
Zlotkey 80 Sales
Cambrault 80 Sales
Errazuriz 80 Sales
Partners 80 Sales
Russell 80 Sales
De Haan 90 Executive
Kochhar 90 Executive
King 90 Executive
Popp 100 Finance
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Urman 100 Finance
Sciarra 100 Finance
Chen 100 Finance
Faviet 100 Finance
Greenberg 100 Finance
Gietz 110 Accounting
Higgins 110 Accounting
Grant
107 rows selected.
②SQL> SELECT e.last_name, e.department_id, d.department_name
FROM employees e RIGHT OUTER JOIN departments d
ON (e.department_id = d.department_id) ;
2 3
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Whalen 10 Administration
Fay 20 Marketing
Hartstein 20 Marketing
Tobias 30 Purchasing
Colmenares 30 Purchasing
Baida 30 Purchasing
Raphaely 30 Purchasing
Khoo 30 Purchasing
Himuro 30 Purchasing
Mavris 40 Human Resources
Feeney 50 Shipping
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Fleaur 50 Shipping
Fripp 50 Shipping
Gates 50 Shipping
Gee 50 Shipping
Geoni 50 Shipping
Grant 50 Shipping
Jones 50 Shipping
Kaufling 50 Shipping
Ladwig 50 Shipping
Everett 50 Shipping
Dilly 50 Shipping
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Dellinger 50 Shipping
Davies 50 Shipping
Chung 50 Shipping
Cabrio 50 Shipping
Bull 50 Shipping
Bissot 50 Shipping
Bell 50 Shipping
Atkinson 50 Shipping
Landry 50 Shipping
Weiss 50 Shipping
Walsh 50 Shipping
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Vollman 50 Shipping
Vargas 50 Shipping
Taylor 50 Shipping
Mallin 50 Shipping
Markle 50 Shipping
Marlow 50 Shipping
Matos 50 Shipping
McCain 50 Shipping
Mikkilineni 50 Shipping
Mourgos 50 Shipping
Nayer 50 Shipping
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
OConnell 50 Shipping
Olson 50 Shipping
Patel 50 Shipping
Perkins 50 Shipping
Philtanker 50 Shipping
Rajs 50 Shipping
Rogers 50 Shipping
Sarchand 50 Shipping
Seo 50 Shipping
Stiles 50 Shipping
Sullivan 50 Shipping
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Austin 60 IT
Pataballa 60 IT
Ernst 60 IT
Lorentz 60 IT
Hunold 60 IT
Baer 70 Public Relations
Abel 80 Sales
Ande 80 Sales
Banda 80 Sales
Bates 80 Sales
Bernstein 80 Sales
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Bloom 80 Sales
Cambrault 80 Sales
Cambrault 80 Sales
Doran 80 Sales
Errazuriz 80 Sales
Fox 80 Sales
Greene 80 Sales
Hall 80 Sales
Hutton 80 Sales
Johnson 80 Sales
King 80 Sales
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Kumar 80 Sales
Lee 80 Sales
Livingston 80 Sales
Marvins 80 Sales
McEwen 80 Sales
Olsen 80 Sales
Ozer 80 Sales
Partners 80 Sales
Russell 80 Sales
Sewall 80 Sales
Smith 80 Sales
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Smith 80 Sales
Sully 80 Sales
Taylor 80 Sales
Tucker 80 Sales
Tuvault 80 Sales
Vishney 80 Sales
Zlotkey 80 Sales
Kochhar 90 Executive
King 90 Executive
De Haan 90 Executive
Popp 100 Finance
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Urman 100 Finance
Chen 100 Finance
Faviet 100 Finance
Sciarra 100 Finance
Greenberg 100 Finance
Gietz 110 Accounting
Higgins 110 Accounting
Treasury
Corporate Tax
Control And Credit
Shareholder Services
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Benefits
Manufacturing
Construction
Contracting
Operations
IT Support
NOC
IT Helpdesk
Government Sales
Retail Sales
Recruiting
LAST_NAME DEPARTMENT_ID DEPARTMENT_NAME
------------------------- ------------- ------------------------------
Payroll
122 rows selected.