MySQL allows a more readable way to combine multiple updates into a single query. This seems to better fit the scenario you describe, is much easier to read, and avoids those difficult-to-untangle multiple conditions.

INSERT INTO table_users (cod_user, date, user_rol, cod_office)
VALUES
('622057', '12082014', 'student', '17389551'),
('2913659', '12082014', 'assistant','17389551'),
('6160230', '12082014', 'admin', '17389551')
ON DUPLICATE KEY UPDATE
 cod_user=VALUES(cod_user), date=VALUES(date)

This assumes that the user_rol, cod_office combination is a primary key. If only one of these is the primary key, then add the other field to the UPDATE list. If neither of them is a primary key (that seems unlikely) then this approach will always create new records - probably not what is wanted.

However, this approach makes prepared statements easier to build and more concise.

INSERT ... ON DUPLICATE KEY UPDATE

You will need to write very complicated conditions if you want to update more than two rows. In such a case you can use INSERT ... ON DUPLICATE KEY UPDATE approach.

INSERT into `mytable` (id, fruit, drink, food)
VALUES
    (1, 'orange', 'water', 'pizza'),
    (2, 'strawberry', 'wine', 'fish'),
    (3, 'peach', 'jiuce', 'cake')
ON DUPLICATE KEY UPDATE
    fruit = VALUES(fruit), 
    drink = VALUES(drink), 
    food = VALUES(food);

One way: SET x=CASE..END (any SQL)

Yes, you can do this, but I doubt that it would improve performances, unless your query has a real large latency.

If the query is indexed on the search value (e.g. if id is the primary key), then locating the desired tuple is very, very fast and after the first query the table will be held in memory.

So, multiple UPDATEs in this case aren't all that bad.

If, on the other hand, the condition requires a full table scan, and even worse, the table's memory impact is significant, then having a single complex query will be better, even if evaluating the UPDATE is more expensive than a simple UPDATE (which gets internally optimized).

In this latter case, you could do:

 UPDATE table SET posX=CASE
      WHEN id=id[1] THEN posX[1]
      WHEN id=id[2] THEN posX[2]
      ...
      ELSE posX END [, posY = CASE ... END]
 WHERE id IN (id[1], id[2], id[3]...);

The total cost is given more or less by: NUM_QUERIES * ( COST_QUERY_SETUP + COST_QUERY_PERFORMANCE ). This way, you knock down on NUM_QUERIES (from N separate id's to 1), but COST_QUERY_PERFORMANCE goes up (about 3x in MySQL 5.28; haven't yet tested in MySQL 8).

Otherwise, I'd try with indexing on id, or modifying the architecture.

This is an example with PHP, where I suppose we have a condition that already requires a full table scan, and which I can use as a key:

// Multiple update rules 
$updates = [
   "fldA='01' AND fldB='X'" => [ 'fldC' => 12, 'fldD' => 15 ],
   "fldA='02' AND fldB='X'" => [ 'fldC' => 60, 'fldD' => 15 ],
   ...
];

The fields updated in the right hand expressions can be one or many, must always be the same (always fldC and fldD in this case). This restriction can be removed, but it would require a modified algorithm.

I can then build the single query through a loop:

$where = [ ];
$set   = [ ];
foreach ($updates as $when => $then) {
    $where[] = "({$when})";
    foreach ($then as $fld => $value) {
       if (!array_key_exists($fld, $set)) {
           $set[$fld] = [ ];
       }
       $set[$fld][] = $value;
    }
}

$set1 = [ ];
foreach ($set as $fld => $values) {
    $set2 = "{$fld} = CASE";
    foreach ($values as $i => $value) {
        $set2 .= " WHEN {$where[$i]} THEN {$value}";
    }
    $set2 .= ' END';
    $set1[] = $set2;
}

// Single query
$sql  = 'UPDATE table SET '
      . implode(', ', $set1)
      . ' WHERE '
      . implode(' OR ', $where);

Another way: ON DUPLICATE KEY UPDATE (MySQL)

In MySQL I think you could do this more easily with a multiple INSERT ON DUPLICATE KEY UPDATE, assuming that id is a primary key keeping in mind that nonexistent conditions ("id = 777" with no 777) will get inserted in the table and maybe cause an error if, for example, other required columns (declared NOT NULL) aren't specified in the query:

INSERT INTO tbl (id, posx, posy, bazinga)
     VALUES (id1, posY1, posY1, 'DELETE'),
     ...
ON DUPLICATE KEY SET posx=VALUES(posx), posy=VALUES(posy);

DELETE FROM tbl WHERE bazinga='DELETE';

The 'bazinga' trick above allows to delete any rows that might have been unwittingly inserted because their id was not present (in other scenarios you might want the inserted rows to stay, though).

For example, a periodic update from a set of gathered sensors, but some sensors might not have been transmitted:

INSERT INTO monitor (id, value)
VALUES (sensor1, value1), (sensor2, 'N/A'), ...
ON DUPLICATE KEY UPDATE value=VALUES(value), reading=NOW();

(This is a contrived case, it would probably be more reasonable to LOCK the table, UPDATE all sensors to N/A and NOW(), then proceed with INSERTing only those values we do have).

A third way: CTE (Any SQL)

This is conceptually almost the same as the INSERT MySQL trick. As written, it works in PostgreSQL 9.6:

WITH updated(id, posX, posY) AS (VALUES
    (id1, posX1, posY1), 
    (id2, posX2, posY2),
    ...
)
UPDATE myTable
    SET 
    posX = updated.posY,
    posY = updated.posY
FROM updated
WHERE (myTable.id = updated.id);

Create a table with the mapping of SKU to new size; update the master table from that.

Many dialects of SQL have a notation for doing updates via joined tables. Some do not. This will work where there is no such notation:

CREATE TABLE SKU_Size_Map
(
     SKU     CHAR(16) NOT NULL,
     Size    INTEGER NOT NULL
);
...Populate this table with the SKU values to be set...
...You must have such a list...

UPDATE MasterTable
   SET Size = (SELECT Size FROM SKU_Size_Map
                WHERE MasterTable.SKU = SKU_Size_Map.Size)
 WHERE SKU IN (SELECT SKU FROM SKU_Size_Map);

The main WHERE condition is need to avoid setting the size to null where there is no matching row.

You can probably also do it with a MERGE statement. But the key insight for any of these notations is that you need a table to do the mapping between SKU and size. You either need a table or you need an algorithm, and the sample data doesn't suggest an algorithm.

You stated that the order of the matching matters but it seems like you don't have anything to ORDER BY in table 1 to create a guaranteed order to match the other table and there is no way in SQL Server to order the rows after insertion date, because information about that is not stored. With this in mind it’s not possible to do a matching with the result you want. There is a solution to update the rows with an arbitrary match within each id. If that would be good enough.

UPDATE t 
SET    t.[date] = tt.[date] 
FROM   (SELECT *, 
               Row_number() 
                 OVER ( 
                   partition BY id 
                   ORDER BY [date]) AS rno 
        FROM   Table1) AS t 
       INNER JOIN (SELECT *, 
                          Row_number() 
                            OVER ( 
                              partition BY id 
                              ORDER BY [date]) AS rno 
                   FROM   Table2) AS tt 
               ON t.id = tt.id 
                  AND t.rno = tt.rno 

This solution will match all rows individually but can't guarantee the order.

DB Fiddle

It's not deterministic. Tom said "random", but that's inexact. As long as the query plan is the same, it is very likely that you will get the same value every time. At least if the plan is serial. But if the plan changes - you could also get a different value.

The important thing is that you cannot rely on anything here. Not even the value being randomly chosen.