SELECT your_columns, COUNT(*) OVER() as Ttl_Rows FROM your_data_set

id	name	Ttl_Rows
1	example	5
2	foo	5
3	bar	5
4	baz	5
5	quux	5

Instead of using two queries to get a count then the line, you can use an aggregate as a window function and use the full result set as the window.
This can be used as a base for further calculation without the complexity of extra self joins.

Let's say I have this data:

Table items

id	name	tag
1	example	unique_tag
2	foo	simple
42	bar	simple
3	baz	hello
51	quux	world

I'd like to get all those lines and know if a tag is used by other lines

SELECT id, name, tag, COUNT(*) OVER (PARTITION BY tag) > 1 AS flag FROM items

The result will be:

id	name	tag	flag
1	example	unique_tag	false
2	foo	simple	true
42	bar	simple	true
3	baz	hello	false
51	quux	world	false

In case your database doesn't have OVER and PARTITION you can use this to produce the same result:

SELECT id, name, tag, (SELECT COUNT(tag) FROM items B WHERE tag = A.tag) > 1 AS flag FROM items A

Given this data:

date	amount
2016-03-12	200
2016-03-11	-50
2016-03-14	100
2016-03-15	100
2016-03-10	-250

SELECT date, amount, SUM(amount) OVER (ORDER BY date ASC) AS running
FROM operations
ORDER BY date ASC

will give you

date	amount	running
2016-03-10	-250	-250
2016-03-11	-50	-300
2016-03-12	200	-100
2016-03-14	100	0
2016-03-15	100	-100

Given this data

User_ID	Completion_Date
1	2016-07-20
1	2016-07-21
2	2016-07-20
2	2016-07-21
2	2016-07-22

;with CTE as
(SELECT *, 
        ROW_NUMBER() OVER (PARTITION BY User_ID 
                           ORDER BY Completion_Date DESC) Row_Num
FROM    Data)
SELECT * FORM CTE WHERE Row_Num <= n

Using n=1, you'll get the one most recent row per user_id:

User_ID	Completion_Date	Row_Num
1	2016-07-21	1
2	2016-07-22	1

Given these sample data:

ID	STATUS	STATUS_TIME	STATUS_BY
1	ONE	2016-09-28-19.47.52.501398	USER_1
3	ONE	2016-09-28-19.47.52.501511	USER_2
1	THREE	2016-09-28-19.47.52.501517	USER_3
3	TWO	2016-09-28-19.47.52.501521	USER_2
3	THREE	2016-09-28-19.47.52.501524	USER_4

Items identified by ID values must move from STATUS 'ONE' to 'TWO' to 'THREE' in sequence, without skipping statuses. The problem is to find users (STATUS_BY) values who violate the rule and move from 'ONE' immediately to 'THREE'.

The LAG() analytical function helps to solve the problem by returning for each row the value in the preceding row:

SELECT * FROM (
 SELECT 
  t.*, 
  LAG(status) OVER (PARTITION BY id ORDER BY status_time) AS prev_status 
  FROM test t
) t1 WHERE status = 'THREE' AND prev_status != 'TWO'

In case your database doesn't have LAG() you can use this to produce the same result:

SELECT A.id, A.status, B.status as prev_status, A.status_time, B.status_time as prev_status_time
FROM Data A, Data B
WHERE A.id = B.id
AND   B.status_time = (SELECT MAX(status_time) FROM Data where status_time < A.status_time and id = A.id)
AND   A.status = 'THREE' AND NOT B.status = 'TWO'