Left join suggestion

Here is a proposed solution.
I have added a third table, just to demonstrate that this could be solved for N tables with a common column.

Prepare demo data:

/* Prepare demo objects */
DROP TABLE IF EXISTS #d, #t1, #t2
CREATE TABLE #d  (DataDate date)
CREATE TABLE #t1 (DataDate date, Value1 float, Value2 float)
CREATE TABLE #t2 (DataDate date, Value3 float, Value4 float)
CREATE TABLE #t3 (DataDate date, Value5 float, Value6 float)

/* Insert demo data */
INSERT INTO #d VALUES ('20181201'),('20181202'),('20181203')
INSERT INTO #t1 VALUES 
    ('20181201', 3.14, 1.18),
    ('20181201', 3.135, 1.185),
    ('20181202', 3.15, 1.19),
    ('20181203', 3.16, 1.195)
INSERT INTO #t2 VALUES 
    ('20181201', 4.14, 2.18),
    ('20181203', 4.15, 2.19),
    ('20181203', 4.1, 2.195)
INSERT INTO #t3 VALUES 
    ('20181201', 3.14, 1.18),
    ('20181201', 3.135, 1.185),
    ('20181202', 3.16, 1.195)

Proposed QUERY Solution:

SELECT
    COALESCE(d.DataDate, t1.datadate, t2.datadate, t3.datadate) AS DataDate
    , t1.Value1
    , t1.Value2
    , t2.Value3
    , t2.Value4
    , t3.Value5
    , t3.Value6
FROM
    (SELECT
        *
        , ROW_NUMBER() OVER (PARTITION BY DataDate ORDER BY (SELECT NULL)) AS rn
    FROM #d) AS d
FULL JOIN
    (SELECT
        *
        , ROW_NUMBER() OVER (PARTITION BY DataDate ORDER BY (SELECT NULL)) AS rn
    FROM #t1) AS t1
    ON (t1.DataDate = d.DataDate AND t1.rn = d.rn)
FULL JOIN
    (SELECT
        *
        , ROW_NUMBER() OVER (PARTITION BY datadate ORDER BY (SELECT NULL)) AS rn
    FROM #t2) AS t2
    ON (t2.DataDate = d.DataDate AND t2.rn = d.rn)
    OR (t2.DataDate = t1.DataDate AND t2.rn = t1.rn)
FULL JOIN
    (SELECT
        *
        , ROW_NUMBER() OVER (PARTITION BY datadate ORDER BY (SELECT NULL)) AS rn
    FROM #t3) AS t3
    ON (t3.DataDate = d.DataDate AND t3.rn = d.rn)
    OR (t3.DataDate = t1.DataDate AND t3.rn = t1.rn)
    OR (t3.DataDate = t2.DataDate AND t3.rn = t2.rn)
ORDER BY DataDate;  

Demo fiddle is posted on db<>fiddle here

DataDate            | Value1 | Value2 | Value3 | Value4 | Value5 | Value6
:------------------ | -----: | -----: | -----: | -----: | -----: | -----:
01/12/2018 00:00:00 |   3.14 |   1.18 |   4.14 |   2.18 |   3.14 |   1.18
01/12/2018 00:00:00 |  3.135 |  1.185 |   null |   null |  3.135 |  1.185
02/12/2018 00:00:00 |   3.15 |   1.19 |   null |   null |   3.16 |  1.195
03/12/2018 00:00:00 |   3.16 |  1.195 |   4.15 |   2.19 |   null |   null
03/12/2018 00:00:00 |   null |   null |    4.1 |  2.195 |   null |   null

Note (optional):

You can greately improve performance by introducing indexes.
As a demo, I have added CLUSTERED INDEXES on DateData column and the preformance increase is significant.

/* Add to improve performance */
CREATE CLUSTERED INDEX CI_DataDate ON #d (DataDate);
CREATE CLUSTERED INDEX CI_DataDate ON #t1 (DataDate);
CREATE CLUSTERED INDEX CI_DataDate ON #t2 (DataDate);
CREATE CLUSTERED INDEX CI_DataDate ON #t3 (DataDate);

Use min if you want the min value (or max depending on what you're looking for) associated by date and t1.value1. Your example, the values are not duplicates so distinct will not work

select #d.DataDate,#t1.Value1,min(#t1.Value2),max(#t2.Value3),min(#t2.Value4)
from #d
    left join #t1 on #d.DataDate = #t1.DataDate
    left join #t2 on #d.DataDate = #t2.DataDate
group by 1,2

if there are exact duplicates that you want to remove then use the following

select distinct #d.DataDate,#t1.Value1,#t1.Value2,#t2.Value3,#t2.Value4
    from #d
        left join #t1 on #d.DataDate = #t1.DataDate
        left join #t2 on #d.DataDate = #t2.DataDate